#importing libraries
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel
C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning. warnings.warn(msg)
def load_data(path,file_name,seperator):
"""
Input : path and file_name
function: loading text file
Output : number of words, document list
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
i=i+1
if text !='':
document = document+text
if text == seperator:
documents_list.append(document)
document = ''
documents_list.append(document)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science.txt",'NEW_PAPER')
Number of words in text file : 120257 Total Number of Documents: 20
#data preprocessing/cleaning
def preprocess_data(doc_set):
"""
Input : list of documents
function: text preprocessing - word tokenize, removing english stopwords, and potter stemming
Output : clean text
"""
# initialize regex tokenizer
word_tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
eng_stop = set(stopwords.words('english'))
# Create p_stemmer of class PorterStemmer
po_stemmer = PorterStemmer()
# list for tokenized documents in loop
texts = []
# loop through document list
for i in doc_set:
# clean and tokenize document string
raw = i.lower()
tokens = word_tokenizer.tokenize(raw)
tokens = [i for i in tokens if len(i)>3]
print(tokens)
# removing english stop words from tokens
stopped_tokens = [i for i in tokens if not i in eng_stop]
# stemmed tokens
stem_tokens = [po_stemmer.stem(i) for i in stopped_tokens]
# list of tokens
texts.append(stem_tokens)
return texts
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'many', 'cases', 'imagesbecome', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'such', 'systems', 'they', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levelsmanuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date', 'publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 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'spatiotemporal', 'covid', '19records', 'fifty', 'eight', 'countries', 'around', 'world', 'whichprovide', 'available', 'covid', 'data', 'sources', 'with', 'minorcode', 'adjustments', 'this', 'toolset', 'accommodate', 'various', 'typesof', 'data', 'published', 'each', 'country', 'various', 'data', 'formats', 'scales', 'channels', 'publish', 'frequencies', 'more', 'importantly', 'countries', 'that', 'provide', 'access', 'historicalcovid', 'data', 'automatically', 'build', 'historical', 'data', 'lections', 'support', 'research', 'repeatedly', 'certain', 'frequency', 'covid', 'scraper', 'processes', 'high', 'effective', 'manner', 'bycollecting', 'data', 'from', 'countries', 'over', 'world', 'within', 'singlerun', 'about', 'minutes', 'after', 'post', 'processing', 'datacleaning', 'fetched', 'data', 'unified', 'saved', 'into', 'databasefor', 'sharing', 'with', 'daily', 'data', 'quality', 'checking', 'data', 'productproduction', 'global', 'covid', 'data', 'github', 'repository', 'hasbeen', 'maintained', 'since', 'march', '2020', 'addition', 'visual', 'ization', 'component', 'developed', 'covid', 'scraper', 'topublish', 'data', 'product', 'service', 'public', 'view', 'andaccess', 'covid', 'scraper', 'utilized', 'scraping', 'technolo', 'gies', 'that', 'used', 'data', 'science', 'related', 'fields', 'integrating', 'open', 'source', 'packages', 'tools', 'dataextracting', 'network', 'simulation', 'file', 'image', 'parsing', 'work', 'flow', 'automation', 'covid', 'scraper', 'highly', 'flexible', 'andautomatic', 'toolset', 'that', 'process', 'tasks', 'unsupervised', 'underusers', 'settings', 'with', 'nature', 'open', 'source', 'users', 'caneasily', 'customize', 'data', 'sources', 'data', 'structure', 'theoutput', 'data', 'product', 'execution', 'logic', 'processing', 'frequency', 'exception', 'handling', 'addition', 'users', 'modify', 'thesource', 'code', 'extend', 'collecting', 'datasets', 'otherpurposes', 'support', 'wider', 'studies', 'tasks', 'such', 'emer', 'gency', 'response', 'natural', 'disaster', 'detection', 'savinglives', 'currently', 'limitation', 'that', 'data', 'quality', 'control', 'andvalidation', 'cannot', 'fully', 'automated', 'because', 'accuracyof', 'parsing', 'text', 'extracting', 'cannot', 'always', 'guaranteedby', 'using', 'current', 'packages', 'hence', 'users', 'need', 'intervenein', 'data', 'quality', 'control', 'process', 'image', 'typedata', 'make', 'sure', 'data', 'product', 'high', 'quality', 'with', 'therapid', 'development', 'text', 'parsing', 'from', 'images', 'will', 'keepupdating', 'this', 'component', 'minimize', 'human', 'interventionin', 'automation', 'process']
# preparing corpus for LSA
def prepare_corpus(doc_clean):
"""
Input : preprossed document
function: creating term dictionary and document term matrix
Output : term dictionary and Document Term Matrix
"""
# every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
term_dictionary = corpora.Dictionary(doc_clean)
# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
docu_term_matrix = [term_dictionary.doc2bow(doc) for doc in doc_clean]
# generate LDA model
return term_dictionary,docu_term_matrix
def create_gensim_lsa_model(doc_clean,number_of_topics,words):
"""
Input : preprocessed document, number of topics and word count
function: create LSA model using gensim
Output : return LSA model
"""
term_dictionary,docu_term_matrix=prepare_corpus(doc_clean)
# LSA model
lsamodel = LsiModel(docu_term_matrix, num_topics=number_of_topics, id2word = term_dictionary) # train model
return lsamodel
# LSA Model
number_of_topics=20
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_covid_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)
#top 5 words from lsa
dict(model_science.show_topic(0, topn=5))
{'covid': 0.4819559123739421,
'imag': 0.3305244842801084,
'data': 0.25927028676543307,
'use': 0.23373910286534985,
'model': 0.2237104216675572}
#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph12.png')
# cosinesimilarity matrix of top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_covid_science =dict(model_science.show_topic(i, topn=words))
b=pd.DataFrame(words_covid_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled15.png')
cosine-similarity
covid imag data use model
covid 1 0.19987 0.36023 0.315797 0.0639177
imag 0.19987 1 -0.115461 0.0796236 -0.101251
data 0.36023 -0.115461 1 -0.0217574 0.0207809
use 0.315797 0.0796236 -0.0217574 1 0.0631737
model 0.0639177 -0.101251 0.0207809 0.0631737 1
#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig15.png')
#loading document
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt",'NEW NEWSPAPER')
Number of words in text file : 8713 Total Number of Documents: 20
#data preprocessing
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'realityby', 'reuterspublished', 'july', '2020', 'updated', 'july', '2020by', 'marc', 'joneslondon', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 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'learning', 'sport', 'more', 'than', 'parent', 'attend', 'community', 'sporting', 'activities', 'physical', 'distancing', 'least', 'metres', 'maintained', 'accommodation', 'facilities', 'overnight', 'event', 'organisers', 'must', 'implement', 'covid', 'safety', 'plan', 'carpooling', 'should', 'continue', 'avoidednew', 'newspaper'] ['zealand', 'launches', 'covid', 'diary', 'appby', 'australian', 'associated', 'presspublished', '2020', 'updated', '2020belatedly', 'cases', 'covid', 'ease', 'zealand', 'government', 'launched', 'covid', 'help', 'kiwis', 'track', 'their', 'movements', 'prime', 'minister', 'jacinda', 'ardern', 'launched', 'wednesday', 'labelling', 'digital', 'diary', 'differs', 'from', 'australian', 'version', 'linking', 'other', 'users', 'instead', 'allows', 'kiwis', 'check', 'different', 'venues', 'such', 'cafes', 'restaurants', 'avoid', 'onerous', 'writing', 'down', 'contact', 'details', 'every', 'place', 'visited', 'helps', 'users', 'when', 'they', 'about', 'keep', 'their', 'movements', 'ardern', 'said', 'this', 'that', 'people', 'that', 'keeps', 'data', 'themselves', 'rather', 'than', 'adding', 'into', 'more', 'broader', 'repository', 'that', 'might', 'held', 'business', 'this', 'stage', 'data', 'sent', 'government', 'authorities', 'from', 'though', 'that', 'functionality', 'will', 'added', 'update', 'next', 'month', 'different', 'functionality', 'means', 'there', 'user', 'threshold', 'required', 'useful', 'still', 'plenty', 'kiwis', 'have', 'taken', 'even', 'before', 'officially', 'launched', 'kiwis', 'almost', 'cent', 'population', 'downloaded', 'wednesday', 'director', 'general', 'health', 'ashley', 'bloomfield', 'announced', 'there', 'were', 'cases', 'covid', 'zealand', 'fourth', 'there', 'have', 'been', 'just', 'cases', 'past', 'week', 'overall', 'past', 'fortnight', 'ever', 'decreasing', 'numbers', 'will', 'bars', 'nightclubs', 'open', 'first', 'time', 'since', 'march', 'thursday', 'provided', 'customers', 'seated', 'groups', 'under', 'receive', 'table', 'service', 'ardern', 'will', 'further', 'review', 'caps', 'gathering', 'numbers', 'currently', 'except', 'funerals', 'which', 'monday', 'cabinet', 'meeting', 'plan', 'regularly', 'adjust', 'settings', 'allow', 'more', 'activity', 'when', 'data', 'says', 'safe', 'ardern', 'said', 'showing', 'well', 'virus', 'been', 'squashed', 'over', 'past', 'months', 'just', 'zealander', 'being', 'treated', 'hospital', 'covid', 'does', 'require', 'intensive', 'care', 'newspaper'] ['records', 'covid', 'cases', 'deathsby', 'reuterspublished', 'june', '2021', 'updated', 'june', '2021london', 'june', 'reuters', 'britain', 'reported', 'daily', 'covid', 'cases', 'government', 'figures', 'showed', 'thursday', 'down', 'slightly', 'from', 'reported', 'before', 'which', 'highest', 'daily', 'total', 'since', 'late', 'february', 'britain', 'also', 'reported', 'seven', 'further', 'deaths', 'within', 'days', 'positive', 'covid', 'test', 'from', 'wednesday', 'taking', 'total', 'death', 'toll', 'this', 'measure', 'some', 'british', 'adult', 'population', 'have', 'received', 'doses', 'covid', 'vaccine', 'have', 'received', 'least', 'dose', 'reporting', 'david', 'milliken', 'editing', 'kate', 'holton', 'newspaper'] ['drugs', 'wrongly', 'touted', 'covid', 'treatmentsby', 'australian', 'associated', 'presspublished', 'august', '2020', 'updated', 'august', '2020medicines', 'falsely', 'touted', 'covid', 'treatments', 'hydroxychloroquine', 'used', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'some', 'studies', 'found', 'make', 'things', 'worse', 'remdesivir', 'antiviral', 'medicine', 'that', 'help', 'some', 'people', 'hospital', 'with', 'severe', 'covid', 'recover', 'faster', 'does', 'help', 'people', 'with', 'milder', 'covid', 'dexamethasone', 'reduces', 'inflammation', 'used', 'treat', 'asthma', 'arthritis', 'reduces', 'risk', 'dying', 'people', 'hospital', 'with', 'serious', 'covid', 'that', 'need', 'help', 'breathe', 'doesn', 'help', 'people', 'with', 'less', 'severe', 'covid', 'could', 'make', 'things', 'worse', 'ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'none', 'found', 'effective', 'against', 'covid', 'outside', 'intensive', 'care', 'source', 'medicinewisenew', 'newspaper'] ['covid', 'schools', 'study', 'breakdownby', 'australian', 'associated', 'presspublished', 'april', '2020', 'updated', 'april', '2020what', 'national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'into', 'covid', 'schools', 'found', 'total', 'students', 'staff', 'members', 'from', 'schools', 'were', 'from', 'march', 'april', '2020', 'diagnosed', 'with', 'covid', 'people', 'nine', 'were', 'students', 'nine', 'were', 'staff', 'members', 'those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'over', 'that', 'time', 'period', 'close', 'contacts', 'just', 'people', 'caught', 'covid', 'those', 'people', 'both', 'students', 'pass', 'disease', 'others', 'students', 'passed', 'coronavirus', 'school', 'staff', 'members', 'spread', 'covid', 'within', 'schools', 'thus', 'been', 'very', 'limited', 'virus', 'transmission', 'children', 'schools', 'less', 'than', 'that', 'influenza', 'newspaper'] ['sydney', 'club', 'shut', 'repeat', 'covid', 'breachesby', 'australian', 'associated', 'presspublished', 'october', '2020', 'updated', 'october', '2020the', 'sydney', 'portugal', 'community', 'club', 'being', 'forced', 'shut', 'seven', 'days', 'after', 'failing', 'comply', 'with', 'covid', 'safe', 'regulations', 'even', 'after', 'three', 'warnings', '5000', 'fine', 'liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'said', 'portugal', 'club', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches', 'will', 'close', 'from', 'wednesday', 'week', 'inspectors', 'visited', 'club', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'plan', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'said', 'tuesday', 'since', 'closure', 'order', 'issued', 'however', 'some', 'things', 'club', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'than', 'while', 'guests', 'need', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines', 'what', 'changed', 'having', 'robust', 'digital', 'check', 'processes', 'that', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'case', 'visited', 'venue', 'argeres', 'said', 'service', 'free', 'covid', 'safe', 'check', 'code', 'businesses', 'registered', 'covid', 'safe', 'anyone', 'check', 'using', 'this', 'code', 'even', 'they', 'have', 'service', 'account', 'inspectors', 'from', 'liquor', 'gaming', 'fair', 'trading', 'safework', 'have', 'conducted', '5605', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'four', 'businesses', 'newspaper'] ['tests', 'underway', 'covid', 'treatmentby', 'australian', 'associated', 'presspublished', 'august', '2020', 'updated', 'august', '2020australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'they', 'hope', 'will', 'provide', 'relief', 'those', 'infected', 'with', 'covid', 'limit', 'spread', 'professor', 'david', 'morris', 'international', 'team', 'have', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'that', 'could', 'prevent', 'virus', 'moving', 'into', 'lungs', 'carriers', 'spreading', 'other', 'people', 'trial', 'coronavirus', 'patients', 'being', 'established', 'with', 'melbourne', 'hospital', 'could', 'start', 'next', 'month', 'bromac', 'been', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'that', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells', 'core', 'agents', 'pineapple', 'stem', 'enzyme', 'tested', 'after', 'observed', 'that', 'pigs', 'eating', 'pineapples', 'were', 'resistant', 'particular', 'gastro', 'conditions', 'taken', 'drug', 'development', 'more', 'than', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'with', 'covid', 'professor', 'david', 'morris', 'said', 'statement', 'monday', 'results', 'show', 'drug', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'from', 'infecting', 'other', 'cells', 'hope', 'results', 'will', 'show', 'treatment', 'confine', 'covid', 'nose', 'throat', 'prevent', 'lung', 'infection', 'stop', 'infected', 'patients', 'from', 'passing', 'live', 'virus', 'said', 'hoped', 'infected', 'person', 'could', 'take', 'medication', 'first', 'signs', 'covid', 'stop', 'virus', 'tracks', 'this', 'could', 'provide', 'very', 'safe', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'back', 'their', 'everyday', 'lives', 'researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'work', 'tandem', 'treatment', 'prevention', 'measure']
# LSA Model
number_of_topics=20
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))#dict and encoding matrix values
#dictionary of top 5 words
dict(model_nscience.show_topic(0,topn=5))
{'test': 0.5425296037655409,
'covid': 0.30373216046545803,
'peopl': 0.2994432685604926,
'symptom': 0.18363931537689843,
'case': 0.18310800492471946}
#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph7.png')
# cosine similarity matrix of top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
b=pd.DataFrame(words_covid_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_nscience.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled7.png')
cosine-similarity
test covid peopl symptom case
test 1 0.0622567 0.326012 0.864501 0.32399
covid 0.0622567 1 0.313763 -0.0833804 0.190355
peopl 0.326012 0.313763 1 0.446673 -0.0764638
symptom 0.864501 -0.0833804 0.446673 1 0.284991
case 0.32399 0.190355 -0.0764638 0.284991 1
#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="y")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig7.png')
#data loading
document_list,titles,word_count_psy=load_data("","covid-psychology.txt",'NEW PAPER')
Number of words in text file : 86550 Total Number of Documents: 20
#data preprocessing
clean_text=preprocess_data(document_list)
['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '1177', '0268580920948807journals', 'sagepub', 'home', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '1177', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'intimes', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'falsecontext', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease', 'mukhtar', '2020a', '2020', 'covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'havealso', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated', 'bythese', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took', 'hisown', 'life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed', 'onthe', 'drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'makea', 'distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmedanti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this', 'canalso', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'healthand', 'psychosocial', 'impacts', 'aguilera', '2020', 'rana', '2020a', '2020b', 'these', 'considerations', 'become', 'even', 'more', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe', 'ofpopular', 'opinion', 'spend', 'ever', 'more', 'time', 'social', 'media', 'people', 'trying', 'tomake', 'sense', 'their', 'changed', 'lives', 'self', 'proclaimed', 'celebrities', 'self', 'identified', 'socialmedia', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'theirown', 'subjective', 'interpretations', 'events', 'this', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', '2020', 'scientific', 'studies', 'from', 'previous', 'disease', 'outbreaks', 'have', 'demonstrated', 'that', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', '2019', 'kalichman', '2009', 'individuals', 'endorsemisinformation', 'about', 'disease', 'less', 'likely', 'follow', 'public', 'health', 'instructions', 'duringthe', 'ebola', 'outbreak', '2014', 'respondents', 'gave', 'credence', 'conspiracy', 'theoriesalleged', 'that', 'they', 'would', 'less', 'likely', 'seek', 'support', 'during', 'disease', 'outbreak', 'national', 'cancer', 'institute', '2020', '2019', 'study', 'montanaro', '2020', 'conducted', 'theus', 'over', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', '2020', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'informationabout', 'covid', 'from', 'current', 'administration', 'mainstream', 'media', 'news', 'outlets', 'york', 'times', '2020', 'mukhtar', '113the', 'psychology', 'misinfodemicsfear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actualor', 'perceived', 'threat', 'mertens', '2018', 'however', 'when', 'fear', 'excessive', 'then', 'thismay', 'have', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'like', 'anxiety', 'andphobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'when', 'fear', 'isinsufficient', 'then', 'this', 'also', 'cause', 'harm', 'both', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation', 'ofpolicies', 'that', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonalsafety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'fear', 'fear', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocialissues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'help', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'have', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact', 'efforts', 'curb', 'misinfodemics', 'process', 'have', 'increased', 'because', 'adverseeffects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainablepreventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health', 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'tran', 'carmen', 'twitter', 'geolocation', 'system', 'with', 'applications', 'publichealth', 'workshops', 'twenty', 'seventh', 'aaai', 'conference', 'artificial', 'intelligence', '2013', 'dutheil', 'mondillon', 'navel', 'ptsd', 'second', 'tsunami', 'sars', 'pandemic', 'psychologicalmedicine', 'pages', '2020', 'elsherief', 'kulkarni', 'nguyen', 'wang', 'belding', 'hate', 'lingo', 'target', 'based', 'linguistic', 'analysisof', 'hate', 'speech', 'social', 'media', 'twelfth', 'international', 'aaai', 'conference', 'social', 'media', '2018', 'engle', 'stromme', 'zhou', 'staying', 'home', 'mobility', 'effects', 'covid', 'available', 'ssrn', '2020', 'ferrara', 'covid', 'twitter', 'bots', 'conspiracies', 'social', 'media', 'activism', 'arxiv', 'preprint', 'arxiv', '2004', '09531', '2020', 'ferrara', 'varol', 'davis', 'menczer', 'flammini', 'rise', 'social', 'bots', 'communications', '2016', 'epidemic', 'psychology', 'works', 'twittert', 'fetzer', 'hensel', 'hermle', 'roth', 'coronavirus', 'perceptions', 'economic', 'anxiety', 'review', 'economicsand', 'statistics', 'pages', '2020', 'figea', 'kaati', 'scrivens', 'measuring', 'online', 'affects', 'white', 'supremacy', 'forum', '2016', 'ieee', 'conferenceon', 'intelligence', 'security', 'informatics', 'pages', 'ieee', '2016', 'finlay', 'gilmore', 'covid', 'alcohol', 'dangerous', 'cocktail', '2020', 'fiske', 'cuddy', 'glick', 'universal', 'dimensions', 'social', 'cognition', 'warmth', 'competence', 'trendsin', 'cognitive', 'sciences', '2007', 'frimer', 'schaefer', 'oakes', 'moral', 'actor', 'selfish', 'agent', 'journal', 'personality', 'social', 'psychology', '2014', 'liang', 'saroha', 'fung', 'people', 'react', 'zika', 'virus', 'outbreakson', 'twitter', 'computational', 'content', 'analysis', 'american', 'journal', 'infection', 'control', '1700', '1702', '2016', 'galea', 'merchant', 'lurie', 'mental', 'health', 'consequences', 'covid', 'physical', 'distancing', 'theneed', 'prevention', 'early', 'intervention', 'jama', 'internal', 'medicine', '2020', 'kang', 'liang', 'kruse', 'mapping', 'county', 'level', 'mobility', 'pattern', 'changes', 'unitedstates', 'response', 'covid', 'sigspatial', 'special', '2020', 'gibbs', 'thematic', 'coding', 'categorizing', 'analyzing', 'qualitative', 'data', 'london', 'sage', 'pages', '2007', 'gill', 'french', 'gergle', 'oberlander', 'language', 'emotion', 'short', 'blog', 'texts', 'proceedingsof', '2008', 'conference', 'computer', 'supported', 'cooperative', 'work', 'pages', '2008', 'goffman', 'stigma', 'notes', 'management', 'spoiled', 'identity', 'simon', 'schuster', '2009', 'gonzalez', 'hidalgo', 'barabasi', 'understanding', 'individual', 'human', 'mobility', 'patterns', 'nature', '7196', '2008', 'graham', 'haidt', 'nosek', 'liberals', 'conservatives', 'rely', 'different', 'sets', 'moral', 'foundations', 'journal', 'personality', 'social', 'psychology', '1029', '2009', 'graham', 'haidt', 'koleva', 'motyl', 'iyer', 'wojcik', 'ditto', 'moral', 'foundations', 'theory', 'thepragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages', 'elsevier', '2013', 'grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses', 'togovernors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', 'ssrn', '3578695', '2020', 'hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'bias', 'dietary', 'self', 'report', 'maycompromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology', '1995', 'hochreiter', 'schmidhuber', 'long', 'short', 'term', 'memory', 'neural', 'computation', '1735', '1780', '1997', 'hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'mind', 'volume', 'mcgraw', 'hill', 'york', '2005', 'holmes', 'connor', 'perry', 'tracey', 'wessely', 'arseneault', 'ballard', 'christensen', 'silver', 'everall', 'multidisciplinary', 'research', 'priorities', 'covid', 'pandemic', 'call', 'action', 'mentalhealth', 'science', 'lancet', 'psychiatry', '2020', 'jiang', 'zhou', 'assessment', 'public', 'attention', 'risk', 'perception', 'emotional', 'andbehavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'risk', 'perception', 'emotionaland', 'behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', '2020', '2020', 'huang', 'bidirectional', 'lstm', 'models', 'sequence', 'tagging', 'arxiv', 'preprint', 'arxiv', '1508', '01991', '2015', 'hunt', 'auriemma', 'cashaw', 'self', 'report', 'bias', 'underreporting', 'depression', 'journal', 'ofpersonality', 'assessment', '2003', 'epidemic', 'psychology', 'works', 'twitter', 'husnayain', 'fuad', 'applications', 'google', 'search', 'trends', 'risk', 'communication', 'infectiousdisease', 'management', 'case', 'study', 'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases', '2020', 'jimeno', 'yepes', 'mackinlay', 'chen', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter', 'studies', 'health', 'technology', 'informatics', '2015', 'johnson', 'fendrich', 'modeling', 'sources', 'self', 'report', 'bias', 'survey', 'drug', 'epidemiology', 'annals', 'ofepidemiology', '2005', 'kahn', 'tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'with', 'linguisticinquiry', 'word', 'count', 'american', 'journal', 'psychology', 'pages', '2007', 'khan', 'huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer', '2019', 'kornfield', 'toma', 'shah', 'moon', 'gustafson', 'what', 'before', 'relapse', 'howlanguage', 'peer', 'peer', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'healthcommunication', '1184', '1193', '2018', 'kouzy', 'jaoude', 'kraitem', 'alam', 'karam', 'adib', 'zarka', 'traboulsi', 'andk', 'baddour', 'coronavirus', 'goes', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus', '2020', 'ubler', 'ross', 'wessler', 'avioli', 'death', 'dying', 'jama', '1972', 'goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr', 'cartography', 'geographic', 'information', 'science', '2013', 'wang', 'zhao', 'impact', 'covid', 'epidemic', 'declaration', 'psychologicalconsequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health', '2032', '2020', 'liang', 'effect', 'covid', 'youth', 'mental', 'health', 'psychiatric', 'quarterly', 'pages', '2020', 'goyal', 'joshi', 'chen', 'levy', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta', 'robustly', 'optimized', 'bert', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '1907', '11692', '2019', 'utkepohl', 'introduction', 'multiple', 'time', 'series', 'analysis', 'springer', 'science', 'business', 'media', '2005', 'maslow', 'theory', 'human', 'motivation', 'psychological', 'review', '1943', 'mcconnell', 'banks', 'avian', 'planning', 'possible', 'pandemic', 'risk', 'trading', 'technology', '2005', 'mohammad', 'turney', 'crowdsourcing', 'word', 'emotion', 'association', 'lexicon', 'computational', 'intelligence', '2013', 'moinet', 'pastor', 'satorras', 'barrat', 'effect', 'risk', 'perception', 'epidemic', 'spreading', 'temporal', 'networks', 'physical', 'review', '012313', '2018', 'connor', 'balasubramanyan', 'routledge', 'smith', 'from', 'tweets', 'polls', 'linking', 'text', 'sentimentto', 'public', 'opinion', 'time', 'series', 'fourth', 'international', 'aaai', 'conference', 'weblogs', 'social', 'media', '2010', 'oyeyemi', 'gabarron', 'wynn', 'ebola', 'twitter', 'misinformation', 'dangerous', 'combination', 'g6178', '2014', 'painter', 'political', 'beliefs', 'affect', 'compliance', 'with', 'covid', 'social', 'distancing', 'orders', 'available', 'ssrn3569098', '2020', 'palshikar', 'simple', 'algorithms', 'peak', 'detection', 'time', 'series', 'proc', 'conf', 'advanced', 'dataanalysis', 'business', 'analytics', 'intelligence', 'volume', '2009', 'paszke', 'gross', 'chintala', 'chanan', 'yang', 'devito', 'desmaison', 'antiga', 'lerer', 'automatic', 'differentiation', 'pytorch', 'proceedings', 'advances', 'neural', 'information', 'processing', 'systemsautodiff', 'workshop', '2017', 'epidemic', 'psychology', 'works', 'twitterj', 'pennington', 'socher', 'manning', 'glove', 'global', 'vectors', 'word', 'representation', 'proceedings', 'theconference', 'empirical', 'methods', 'natural', 'language', 'processing', 'pages', '1532', '1543', 'association', 'computationallinguistics', '2014', 'perrin', 'anderson', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'ismostly', 'unchanged', 'since', '2018', 'https', 'pewresearch', 'fact', 'tank', '2019', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'mostly', 'unchanged', 'since', '2018', 'plutchik', 'emotions', 'university', 'press', 'america', '1991', 'pulido', 'villarejo', 'carballido', 'redondo', 'sama', 'omez', 'covid', 'infodemic', 'more', 'retweetsfor', 'science', 'based', 'information', 'coronavirus', 'than', 'false', 'information', 'international', 'sociology', 'page0268580920914755', '2020', 'shen', 'zhao', 'wang', 'nationwide', 'survey', 'psychological', 'distress', 'among', 'chinesepeople', 'covid', 'epidemic', 'implications', 'policy', 'recommendations', 'general', 'psychiatry', '2020', 'rezapour', 'shah', 'diesner', 'enhancing', 'measurement', 'social', 'effects', 'capturing', 'morality', 'inproceedings', 'tenth', 'workshop', 'computational', 'approaches', 'subjectivity', 'sentiment', 'social', 'mediaanalysis', 'pages', '2019', 'russell', 'mining', 'social', 'data', 'mining', 'facebook', 'twitter', 'linkedin', 'google', 'github', 'more', 'reilly', 'media', '2013', 'saif', 'alani', 'alleviating', 'data', 'sparsity', 'twitter', 'sentiment', 'analysis', 'ceur', 'workshop', 'proceedings', 'ceur', '2012', 'salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto', 'khandelwal', 'mabry', 'digital', 'epidemiology', 'plos', 'comput', 'biol', 'e1002616', '2012', 'scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'from', 'social', 'media', 'inproceedings', 'conference', 'health', 'inference', 'learning', 'pages', '2020', 'schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'life', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press', '1973', 'isbn9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c', 'shaw', 'gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason', 'andc', 'lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'with', 'breastcancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer', '2007', 'shen', 'rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'from', 'linguistic', 'signal', 'clinical', 'reality', 'pages', '2017', 'shultz', 'baingana', 'neria', '2014', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response', 'jama', '2015', 'simms', 'ramstedt', 'rich', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortionsthrough', 'machine', 'learning', 'text', 'analytics', '2017', 'ieee', 'international', 'conference', 'healthcare', 'informatics', 'ichi', 'pages', 'ieee', '2017', 'smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association', '2012', 'sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'fake', 'news', 'exploringhealth', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'case', 'study', 'american', 'journalof', 'health', 'education', '2018', 'strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness', '1990', 'tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizationalidentity', '1979', 'tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'liwc', 'computerized', 'text', 'analysismethods', 'journal', 'language', 'social', 'psychology', '2010', 'epidemic', 'psychology', 'works', 'twitter', '21the', 'economist', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international', '2020', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'industry', 'booming', 'bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett', 'crum', 'douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response', 'nature', 'human', 'behaviour', 'pages', '2020', 'varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection', 'estimation', 'characterization', 'eleventh', 'international', 'aaai', 'conference', 'social', 'media', '2017', 'wang', 'immediate', 'psychological', 'responses', 'associatedfactors', 'during', 'initial', 'stage', '2019', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'populationin', 'china', 'international', 'journal', 'environmental', 'research', 'public', 'health', '1729', '2020', 'waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion', 'comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society', '1813', '1831', '2018', 'windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises', 'risk', 'hazards', 'crisis', 'public', 'policy', '2014', 'wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center', '2019', 'wolf', 'theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'onlineactivity', 'journal', 'language', 'social', 'psychology', '2013', 'wood', 'propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', '2015', '2016', 'zika', 'virus', 'outbreak', 'cyberpsychology', 'behavior', 'social', 'networking', '2018', 'yang', 'torres', 'lugo', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during', 'thecovid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '2004', '14484', '2020', 'acknowledgmentswe', 'thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'this', 'researchwas', 'partly', 'supported', 'grant', 'green', 'routes', '869764', 'data', 'availabilitythe', 'daily', 'aggregates', 'measurements', 'available', 'https', '6084', 'figshare', '14892642', 'tweet', 'used', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasetsand', 'visualizations', 'available', 'project', 'site', 'http', 'social', 'dynamics', 'epidemicpsychology', 'competing', 'intereststhe', 'author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twittersupplementary', 'materialsspatial', 'representativeness', 'twitter', 'data', 'make', 'sure', 'that', 'geographical', 'penetration', 'twitterdata', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'user', 'activity', 'each', 'state', 'withthe', 'census', 'population', 'figure', 'shows', 'that', 'both', 'number', 'twitter', 'users', 'volume', 'their', 'tweetscorrelate', 'very', 'strongly', 'with', 'census', 'population', 'population', 'estimates', 'year', '2019', 'level', 'states', 'https', 'census', '17log', 'population', '1213141516log', 'tweets', '939r', '08814', '17log', 'population', '91011121314log', 'users', '952r', '106figure', 'correlation', 'census', 'population', 'states', 'estimated', 'year', '2019', 'with', 'thenumber', 'tweets', 'posted', 'those', 'states', 'left', 'number', 'unique', 'twitter', 'users', 'postedat', 'least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'with', 'thecoefficient', 'determination', 'linear', 'regression', 'pearson', 'correlation', 'coefficient', 'andthe', 'coefficient', 'number', 'tweets', 'that', 'twitter', 'users', 'scale', 'almost', 'linearlywith', 'population', 'estimates', 'signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'that', 'first', 'phase', 'wascharacterized', 'discussions', 'that', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones', 'wemeasured', 'volume', 'tweets', 'containing', 'hashtags', 'that', 'were', 'clear', 'indicators', 'content', 'that', 'either', 'racist', 'oraimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'from', 'list', 'hashtags', 'that', 'appeared', 'atleast', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'over', 'time', 'theypeak', 'during', 'first', 'phase', 'when', 'fear', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asianforeigners', 'were', 'their', 'peaks', 'breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'into', 'regions', 'thatwere', 'dealing', 'with', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assesswhether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed', 'themeasures', 'that', 'represent', 'relative', 'presence', 'three', 'phases', 'over', 'time', 'refusal', 'anger', 'acceptance', 'each', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curvesindicate', 'that', 'three', 'phases', 'took', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states', 'refusal', 'followed', 'anger', 'which', 'then', 'followed', 'acceptance', 'there', 'very', 'little', 'variation', 'thetimes', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']
# LSA Model
number_of_topics=20
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)
#top 5 words from lsa
dict(model_psy.show_topic(0,topn=5))
{'2020': 0.42727157511952374,
'covid': 0.32099481957500275,
'psycholog': 0.22665575223350956,
'social': 0.21129956668978256,
'epidem': 0.16257116253243872}
#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph27.png')
# cosine similarity matrix for top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model_psy.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled8.png')
cosine-similarity
2020 covid psycholog social epidem
2020 1 0.282638 0.146216 0.086535 0.0550711
covid 0.282638 1 0.137066 0.0146927 -0.0702621
psycholog 0.146216 0.137066 1 0.116781 0.101001
social 0.086535 0.0146927 0.116781 1 0.434874
epidem 0.0550711 -0.0702621 0.101001 0.434874 1
#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig8.png')
# defining mantel test function
def mantel_test(n):
#all words from corpus
words_science = dict(model_science.show_topic(0, topn=word_count_science))
words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))
#top words from the corpus
top_words_science = dict(model_science.show_topic(0, topn=n))
top_words_psy = dict(model_psy.show_topic(0,topn=n))
#Finding out the weights of top 5 law words in covid science and updating their weights w.r.t psychology dataset
c={}
for key in top_words_science:
try:
c[key] = words_psy[key]
except KeyError:
c[key] = 0
top_words_psy.update(c)
#Finding out the weights of top 5 law words in covid psychology and updating their weights w.r.t covid science dataset
b={}
for key in top_words_psy:
try:
b[key] = words_science[key]
except KeyError:
b[key] = 0
b.update(top_words_science)
top_words_science=b
#pairwise distance for top 10 words in covid science
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
b=pd.DataFrame(words_from_science,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_science.keys()),
index = list(top_words_science.keys()))
l = list(top_words_science.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df1 = df.to_numpy()
#pairwise distace of top 10 words in psychology dataset
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
b=pd.DataFrame(words_from_psy,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_psy.keys()),
index = list(top_words_psy.keys()))
l = list(top_words_psy.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df2 = df.to_numpy()
dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])
dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])
return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')
mantel_test(5)
Pairwise-distance
2020 covid psycholog social epidem \
2020 0 0.674002 0.36767 0.531836 0.396413
covid 0.674002 0 0.629265 0.734645 0.650745
psycholog 0.36767 0.629265 8.23181e-11 0.400991 0.102067
social 0.531836 0.734645 0.400991 0 0.418365
epidem 0.396413 0.650745 0.102067 0.418365 1.86265e-09
imag 0.891661 0.906757 0.790683 0.916729 0.795824
data 0.857554 0.768464 0.722907 0.832332 0.723699
use 0.555855 0.657712 0.474182 0.666313 0.488571
model 0.711022 0.902144 0.68823 0.806082 0.669871
imag data use model
2020 0.891661 0.857554 0.555855 0.711022
covid 0.906757 0.768464 0.657712 0.902144
psycholog 0.790683 0.722907 0.474182 0.68823
social 0.916729 0.832332 0.666313 0.806082
epidem 0.795824 0.723699 0.488571 0.669871
imag 1.49012e-08 1.13106 0.889194 1.09935
data 1.13106 1.49012e-08 0.873157 0.987321
use 0.889194 0.873157 1.05367e-08 0.810734
model 1.09935 0.987321 0.810734 0
Pairwise-distance
2020 covid psycholog social epidem imag \
2020 0 0.735209 0.749397 0.760129 0.753629 0.65016
covid 0.735209 0 0.701633 0.732958 0.740581 0.569647
psycholog 0.749397 0.701633 1.05367e-08 0.628189 0.611607 0.494348
social 0.760129 0.732958 0.628189 0 0.470988 0.446562
epidem 0.753629 0.740581 0.611607 0.470988 0 0.430313
imag 0.65016 0.569647 0.494348 0.446562 0.430313 0
data 0.699952 0.503389 0.515389 0.49647 0.436326 0.203807
use 0.691721 0.588164 0.439474 0.445464 0.388983 0.249131
model 0.621761 0.572642 0.523753 0.420748 0.366641 0.169949
data use model
2020 0.699952 0.691721 0.621761
covid 0.503389 0.588164 0.572642
psycholog 0.515389 0.439474 0.523753
social 0.49647 0.445464 0.420748
epidem 0.436326 0.388983 0.366641
imag 0.203807 0.249131 0.169949
data 3.72529e-09 0.221334 0.241608
use 0.221334 0 0.191126
model 0.241608 0.191126 0
(-0.533093121849338, 0.95076, -1.6363094990614924)
#mantel test with 20 word network
mantel_test(10)
Pairwise-distance
2020 covid psycholog social epidem pandem \
2020 0 0.674002 0.36767 0.531836 0.396413 0.418207
covid 0.674002 0 0.629265 0.734645 0.650745 0.647843
psycholog 0.36767 0.629265 8.23181e-11 0.400991 0.102067 0.172896
social 0.531836 0.734645 0.400991 0 0.418365 0.368846
epidem 0.396413 0.650745 0.102067 0.418365 1.86265e-09 0.132024
pandem 0.418207 0.647843 0.172896 0.368846 0.132024 0
health 0.371036 0.615536 0.172764 0.403539 0.189056 0.204128
time 0.490966 0.687675 0.350242 0.493539 0.309363 0.378335
group 0.37055 0.613001 0.0774391 0.394031 0.133837 0.188081
peopl 0.348986 0.664598 0.258756 0.34618 0.284587 0.278945
imag 0.891661 0.906757 0.790683 0.916729 0.795824 0.816846
data 0.857554 0.768464 0.722907 0.832332 0.723699 0.718704
use 0.555855 0.657712 0.474182 0.666313 0.488571 0.509923
model 0.711022 0.902144 0.68823 0.806082 0.669871 0.687796
dataset 0.715219 0.890297 0.573152 0.662778 0.603267 0.612139
class 0.569307 0.64165 0.48671 0.658691 0.507188 0.543696
learn 0.650257 0.749877 0.538634 0.701563 0.540577 0.547968
perform 0.45835 0.657498 0.343949 0.560799 0.3769 0.392621
train 0.458609 0.593987 0.325781 0.528955 0.330647 0.333289
health time group peopl imag data \
2020 0.371036 0.490966 0.37055 0.348986 0.891661 0.857554
covid 0.615536 0.687675 0.613001 0.664598 0.906757 0.768464
psycholog 0.172764 0.350242 0.0774391 0.258756 0.790683 0.722907
social 0.403539 0.493539 0.394031 0.34618 0.916729 0.832332
epidem 0.189056 0.309363 0.133837 0.284587 0.795824 0.723699
pandem 0.204128 0.378335 0.188081 0.278945 0.816846 0.718704
health 0 0.379101 0.177002 0.308461 0.823753 0.741774
time 0.379101 0 0.335529 0.413296 0.864039 0.747668
group 0.177002 0.335529 0 0.267843 0.784342 0.728073
peopl 0.308461 0.413296 0.267843 0 0.854849 0.778094
imag 0.823753 0.864039 0.784342 0.854849 1.49012e-08 1.13106
data 0.741774 0.747668 0.728073 0.778094 1.13106 1.49012e-08
use 0.49672 0.544303 0.45891 0.532211 0.889194 0.873157
model 0.696478 0.702871 0.710612 0.748253 1.09935 0.987321
dataset 0.601041 0.716844 0.597873 0.630252 0.884121 0.853085
class 0.519957 0.654512 0.522106 0.564029 0.91668 0.910914
learn 0.539503 0.670617 0.532541 0.625019 0.9801 0.956935
perform 0.399797 0.551318 0.353306 0.420931 0.844045 0.787457
train 0.371207 0.490915 0.337769 0.413696 0.709201 0.804153
use model dataset class learn perform \
2020 0.555855 0.711022 0.715219 0.569307 0.650257 0.45835
covid 0.657712 0.902144 0.890297 0.64165 0.749877 0.657498
psycholog 0.474182 0.68823 0.573152 0.48671 0.538634 0.343949
social 0.666313 0.806082 0.662778 0.658691 0.701563 0.560799
epidem 0.488571 0.669871 0.603267 0.507188 0.540577 0.3769
pandem 0.509923 0.687796 0.612139 0.543696 0.547968 0.392621
health 0.49672 0.696478 0.601041 0.519957 0.539503 0.399797
time 0.544303 0.702871 0.716844 0.654512 0.670617 0.551318
group 0.45891 0.710612 0.597873 0.522106 0.532541 0.353306
peopl 0.532211 0.748253 0.630252 0.564029 0.625019 0.420931
imag 0.889194 1.09935 0.884121 0.91668 0.9801 0.844045
data 0.873157 0.987321 0.853085 0.910914 0.956935 0.787457
use 1.05367e-08 0.810734 0.697269 0.660308 0.660297 0.473797
model 0.810734 0 0.895999 0.816373 0.860437 0.567306
dataset 0.697269 0.895999 0 0.715456 0.822228 0.673615
class 0.660308 0.816373 0.715456 0 0.698663 0.631055
learn 0.660297 0.860437 0.822228 0.698663 0 0.599322
perform 0.473797 0.567306 0.673615 0.631055 0.599322 5.26836e-09
train 0.615921 0.69883 0.684229 0.564319 0.578432 0.40463
train
2020 0.458609
covid 0.593987
psycholog 0.325781
social 0.528955
epidem 0.330647
pandem 0.333289
health 0.371207
time 0.490915
group 0.337769
peopl 0.413696
imag 0.709201
data 0.804153
use 0.615921
model 0.69883
dataset 0.684229
class 0.564319
learn 0.578432
perform 0.40463
train 0
Pairwise-distance
2020 covid psycholog social epidem pandem \
2020 0 0.735209 0.749397 0.760129 0.753629 0.541543
covid 0.735209 0 0.701633 0.732958 0.740581 0.557089
psycholog 0.749397 0.701633 1.05367e-08 0.628189 0.611607 0.527426
social 0.760129 0.732958 0.628189 0 0.470988 0.592742
epidem 0.753629 0.740581 0.611607 0.470988 0 0.600247
pandem 0.541543 0.557089 0.527426 0.592742 0.600247 7.45058e-09
health 0.731581 0.861669 0.631573 0.749823 0.75502 0.630955
time 0.615462 0.632654 0.511761 0.410078 0.440071 0.474871
group 1.0016 0.936565 0.91457 0.798941 0.906831 0.860574
peopl 0.83341 0.739808 0.686906 0.539341 0.690625 0.558758
imag 0.65016 0.569647 0.494348 0.446562 0.430313 0.359011
data 0.699952 0.503389 0.515389 0.49647 0.436326 0.368065
use 0.691721 0.588164 0.439474 0.445464 0.388983 0.428385
model 0.621761 0.572642 0.523753 0.420748 0.366641 0.408663
dataset 0.64451 0.578329 0.486274 0.457292 0.419513 0.367752
class 0.649637 0.582104 0.485732 0.46168 0.419733 0.373695
learn 0.602956 0.581803 0.504455 0.467341 0.418748 0.360697
perform 0.686484 0.613461 0.524585 0.50715 0.470766 0.396873
train 0.629644 0.580622 0.494149 0.49852 0.445494 0.375578
health time group peopl imag \
2020 0.731581 0.615462 1.0016 0.83341 0.65016
covid 0.861669 0.632654 0.936565 0.739808 0.569647
psycholog 0.631573 0.511761 0.91457 0.686906 0.494348
social 0.749823 0.410078 0.798941 0.539341 0.446562
epidem 0.75502 0.440071 0.906831 0.690625 0.430313
pandem 0.630955 0.474871 0.860574 0.558758 0.359011
health 0 0.668585 1.00445 0.843911 0.625065
time 0.668585 5.26836e-09 0.802166 0.536735 0.266881
group 1.00445 0.802166 0 0.788851 0.767416
peopl 0.843911 0.536735 0.788851 7.45058e-09 0.495739
imag 0.625065 0.266881 0.767416 0.495739 0
data 0.682985 0.347752 0.788488 0.57412 0.203807
use 0.705662 0.342372 0.813728 0.582681 0.249131
model 0.6665 0.254812 0.795447 0.53675 0.169949
dataset 0.625121 0.264936 0.762475 0.499407 0.0400685
class 0.622407 0.267353 0.760009 0.489682 0.052582
learn 0.646428 0.282943 0.769878 0.507473 0.102833
perform 0.667897 0.293216 0.793514 0.550034 0.202954
train 0.643295 0.288389 0.785966 0.516929 0.134089
data use model dataset class \
2020 0.699952 0.691721 0.621761 0.64451 0.649637
covid 0.503389 0.588164 0.572642 0.578329 0.582104
psycholog 0.515389 0.439474 0.523753 0.486274 0.485732
social 0.49647 0.445464 0.420748 0.457292 0.46168
epidem 0.436326 0.388983 0.366641 0.419513 0.419733
pandem 0.368065 0.428385 0.408663 0.367752 0.373695
health 0.682985 0.705662 0.6665 0.625121 0.622407
time 0.347752 0.342372 0.254812 0.264936 0.267353
group 0.788488 0.813728 0.795447 0.762475 0.760009
peopl 0.57412 0.582681 0.53675 0.499407 0.489682
imag 0.203807 0.249131 0.169949 0.0400685 0.052582
data 3.72529e-09 0.221334 0.241608 0.212732 0.220608
use 0.221334 0 0.191126 0.236281 0.242491
model 0.241608 0.191126 0 0.151736 0.164574
dataset 0.212732 0.236281 0.151736 0 0.0293471
class 0.220608 0.242491 0.164574 0.0293471 4.65661e-10
learn 0.231899 0.250769 0.177662 0.0908676 0.0919756
perform 0.279963 0.317854 0.257718 0.20024 0.20535
train 0.246193 0.279958 0.211779 0.12333 0.12912
learn perform train
2020 0.602956 0.686484 0.629644
covid 0.581803 0.613461 0.580622
psycholog 0.504455 0.524585 0.494149
social 0.467341 0.50715 0.49852
epidem 0.418748 0.470766 0.445494
pandem 0.360697 0.396873 0.375578
health 0.646428 0.667897 0.643295
time 0.282943 0.293216 0.288389
group 0.769878 0.793514 0.785966
peopl 0.507473 0.550034 0.516929
imag 0.102833 0.202954 0.134089
data 0.231899 0.279963 0.246193
use 0.250769 0.317854 0.279958
model 0.177662 0.257718 0.211779
dataset 0.0908676 0.20024 0.12333
class 0.0919756 0.20535 0.12912
learn 1.86265e-09 0.213694 0.103606
perform 0.213694 0 0.162208
train 0.103606 0.162208 0
(-0.513948348056973, 0.99636, -2.35975177848905)
# mantel test for covid science and non science dataset(Not necessary for experimental results of project)
def mantel_test_n(n):
words_science = dict(model_science.show_topic(0, topn=word_count_science))
words_nscience = dict(model_nscience.show_topic(0, topn=word_count_nscience))
top_words_science = dict(model_science.show_topic(0, topn=n))
top_words_nscience = dict(model_nscience.show_topic(0,topn=n))
c={} #law words in non law
for key in top_words_science:
try:
c[key] = words_nscience[key]
except KeyError:
c[key] = 0
top_words_nscience.update(c)
b={}
for key in top_words_nscience:
try:
b[key] = words_science[key]
except KeyError:
b[key] = 0
b.update(top_words_science)
top_words_science=b
#pairwise distance
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
b=pd.DataFrame(words_from_science,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_science.keys()),
index = list(top_words_science.keys()))
l = list(top_words_science.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df1 = df.to_numpy()
#pairwise distace for non-law
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_nscience =dict(model_nscience.show_topic(i, topn=word_count_nscience))
b=pd.DataFrame(words_from_nscience,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_nscience.keys()),
index = list(top_words_nscience.keys()))
l = list(top_words_nscience.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df2 = df.to_numpy()
dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])
dist2 = list(df2[np.triu_indices(len(top_words_nscience), k = 1)])
return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')
mantel_test_n(5)
Pairwise-distance
test covid peopl symptom case imag \
test 1.05367e-08 0.750979 0.491932 0.533855 0.798228 0.987696
covid 0.750979 0 0.664598 0.621303 0.822785 0.906757
peopl 0.491932 0.664598 0 0.270493 0.579838 0.854849
symptom 0.533855 0.621303 0.270493 0 0.518144 0.792381
case 0.798228 0.822785 0.579838 0.518144 0 0.940637
imag 0.987696 0.906757 0.854849 0.792381 0.940637 1.49012e-08
data 0.893981 0.768464 0.778094 0.726166 0.804397 1.13106
use 0.665969 0.657712 0.532211 0.457572 0.667066 0.889194
model 0.860115 0.902144 0.748253 0.692453 0.791349 1.09935
data use model
test 0.893981 0.665969 0.860115
covid 0.768464 0.657712 0.902144
peopl 0.778094 0.532211 0.748253
symptom 0.726166 0.457572 0.692453
case 0.804397 0.667066 0.791349
imag 1.13106 0.889194 1.09935
data 1.49012e-08 0.873157 0.987321
use 0.873157 1.05367e-08 0.810734
model 0.987321 0.810734 0
Pairwise-distance
test covid peopl symptom case imag \
test 0 0.960096 0.707701 0.474889 0.670867 0
covid 0.960096 1.49012e-08 0.74718 0.786907 0.76285 0
peopl 0.707701 0.74718 0 0.466821 0.691487 0
symptom 0.474889 0.786907 0.466821 5.26836e-09 0.421852 0
case 0.670867 0.76285 0.691487 0.421852 0 0
imag 0 0 0 0 0 0
data 0.745108 0.755606 0.594656 0.360268 0.401465 0
use 0.742916 0.665757 0.515411 0.330993 0.45149 0
model 0.681393 0.724479 0.522393 0.256259 0.420446 0
data use model
test 0.745108 0.742916 0.681393
covid 0.755606 0.665757 0.724479
peopl 0.594656 0.515411 0.522393
symptom 0.360268 0.330993 0.256259
case 0.401465 0.45149 0.420446
imag 0 0 0
data 3.72529e-09 0.26882 0.252962
use 0.26882 3.72529e-09 0.202653
model 0.252962 0.202653 6.58545e-10
(-0.45038456855907627, 0.90518, -1.3821255209451901)
mantel_test_n(10)
Pairwise-distance
test covid peopl symptom case travel \
test 1.05367e-08 0.750979 0.491932 0.533855 0.798228 0.533196
covid 0.750979 0 0.664598 0.621303 0.822785 0.62861
peopl 0.491932 0.664598 0 0.270493 0.579838 0.259909
symptom 0.533855 0.621303 0.270493 0 0.518144 0.0647461
case 0.798228 0.822785 0.579838 0.518144 0 0.529687
travel 0.533196 0.62861 0.259909 0.0647461 0.529687 0
fever 0.528434 0.619159 0.266031 0.0488287 0.534801 0.0389511
infect 0.642507 0.715799 0.495376 0.413898 0.581321 0.411621
contact 0.493119 0.628239 0.235357 0.0944009 0.533702 0.100415
respiratori 0.53334 0.624215 0.266039 0.0607611 0.519514 0.0466501
imag 0.987696 0.906757 0.854849 0.792381 0.940637 0.792253
data 0.893981 0.768464 0.778094 0.726166 0.804397 0.71877
use 0.665969 0.657712 0.532211 0.457572 0.667066 0.473015
model 0.860115 0.902144 0.748253 0.692453 0.791349 0.687141
dataset 0.751918 0.890297 0.630252 0.566885 0.699982 0.573304
class 0.709419 0.64165 0.564029 0.505606 0.715628 0.48807
learn 0.782049 0.749877 0.625019 0.552334 0.763608 0.530465
perform 0.619943 0.657498 0.420931 0.34055 0.644937 0.343634
train 0.64225 0.593987 0.413696 0.336186 0.607643 0.323423
fever infect contact respiratori imag \
test 0.528434 0.642507 0.493119 0.53334 0.987696
covid 0.619159 0.715799 0.628239 0.624215 0.906757
peopl 0.266031 0.495376 0.235357 0.266039 0.854849
symptom 0.0488287 0.413898 0.0944009 0.0607611 0.792381
case 0.534801 0.581321 0.533702 0.519514 0.940637
travel 0.0389511 0.411621 0.100415 0.0466501 0.792253
fever 0 0.406866 0.0966737 0.0446772 0.795248
infect 0.406866 7.45058e-09 0.418462 0.416387 0.917136
contact 0.0966737 0.418462 0 0.101398 0.805435
respiratori 0.0446772 0.416387 0.101398 6.58545e-10 0.785179
imag 0.795248 0.917136 0.805435 0.785179 1.49012e-08
data 0.72522 0.845112 0.730394 0.728012 1.13106
use 0.455654 0.69464 0.485141 0.45702 0.889194
model 0.696961 0.748659 0.699363 0.682944 1.09935
dataset 0.578419 0.782891 0.58464 0.577963 0.884121
class 0.49579 0.66437 0.507371 0.490767 0.91668
learn 0.539896 0.604111 0.567556 0.54391 0.9801
perform 0.338136 0.582515 0.362636 0.324424 0.844045
train 0.328509 0.482453 0.34788 0.316082 0.709201
data use model dataset class learn \
test 0.893981 0.665969 0.860115 0.751918 0.709419 0.782049
covid 0.768464 0.657712 0.902144 0.890297 0.64165 0.749877
peopl 0.778094 0.532211 0.748253 0.630252 0.564029 0.625019
symptom 0.726166 0.457572 0.692453 0.566885 0.505606 0.552334
case 0.804397 0.667066 0.791349 0.699982 0.715628 0.763608
travel 0.71877 0.473015 0.687141 0.573304 0.48807 0.530465
fever 0.72522 0.455654 0.696961 0.578419 0.49579 0.539896
infect 0.845112 0.69464 0.748659 0.782891 0.66437 0.604111
contact 0.730394 0.485141 0.699363 0.58464 0.507371 0.567556
respiratori 0.728012 0.45702 0.682944 0.577963 0.490767 0.54391
imag 1.13106 0.889194 1.09935 0.884121 0.91668 0.9801
data 1.49012e-08 0.873157 0.987321 0.853085 0.910914 0.956935
use 0.873157 1.05367e-08 0.810734 0.697269 0.660308 0.660297
model 0.987321 0.810734 0 0.895999 0.816373 0.860437
dataset 0.853085 0.697269 0.895999 0 0.715456 0.822228
class 0.910914 0.660308 0.816373 0.715456 0 0.698663
learn 0.956935 0.660297 0.860437 0.822228 0.698663 0
perform 0.787457 0.473797 0.567306 0.673615 0.631055 0.599322
train 0.804153 0.615921 0.69883 0.684229 0.564319 0.578432
perform train
test 0.619943 0.64225
covid 0.657498 0.593987
peopl 0.420931 0.413696
symptom 0.34055 0.336186
case 0.644937 0.607643
travel 0.343634 0.323423
fever 0.338136 0.328509
infect 0.582515 0.482453
contact 0.362636 0.34788
respiratori 0.324424 0.316082
imag 0.844045 0.709201
data 0.787457 0.804153
use 0.473797 0.615921
model 0.567306 0.69883
dataset 0.673615 0.684229
class 0.631055 0.564319
learn 0.599322 0.578432
perform 5.26836e-09 0.40463
train 0.40463 0
Pairwise-distance
test covid peopl symptom case travel \
test 0 0.960096 0.707701 0.474889 0.670867 0.533249
covid 0.960096 1.49012e-08 0.74718 0.786907 0.76285 0.780415
peopl 0.707701 0.74718 0 0.466821 0.691487 0.499547
symptom 0.474889 0.786907 0.466821 5.26836e-09 0.421852 0.1642
case 0.670867 0.76285 0.691487 0.421852 0 0.455464
travel 0.533249 0.780415 0.499547 0.1642 0.455464 0
fever 0.516369 0.767072 0.466238 0.0602064 0.423661 0.14079
infect 0.777339 0.815879 0.713788 0.553901 0.647398 0.522268
contact 0.59684 0.716573 0.501878 0.275565 0.452727 0.306657
respiratori 0.529887 0.76097 0.467876 0.0763009 0.418966 0.137889
imag 0 0 0 0 0 0
data 0.745108 0.755606 0.594656 0.360268 0.401465 0.343604
use 0.742916 0.665757 0.515411 0.330993 0.45149 0.29489
model 0.681393 0.724479 0.522393 0.256259 0.420446 0.222238
dataset 0 0 0 0 0 0
class 0.681496 0.726887 0.505246 0.260397 0.4196 0.227429
learn 0.683699 0.717042 0.532415 0.262241 0.416605 0.234838
perform 0 0 0 0 0 0
train 0.680553 0.722203 0.524164 0.256927 0.417977 0.222096
fever infect contact respiratori imag data \
test 0.516369 0.777339 0.59684 0.529887 0 0.745108
covid 0.767072 0.815879 0.716573 0.76097 0 0.755606
peopl 0.466238 0.713788 0.501878 0.467876 0 0.594656
symptom 0.0602064 0.553901 0.275565 0.0763009 0 0.360268
case 0.423661 0.647398 0.452727 0.418966 0 0.401465
travel 0.14079 0.522268 0.306657 0.137889 0 0.343604
fever 0 0.533792 0.251857 0.0184378 0 0.323235
infect 0.533792 0 0.603863 0.526883 0 0.564586
contact 0.251857 0.603863 0 0.24682 0 0.338266
respiratori 0.0184378 0.526883 0.24682 0 0 0.311659
imag 0 0 0 0 0 0
data 0.323235 0.564586 0.338266 0.311659 0 3.72529e-09
use 0.289428 0.571065 0.345758 0.276271 0 0.26882
model 0.204474 0.491545 0.268131 0.186408 0 0.252962
dataset 0 0 0 0 0 0
class 0.208538 0.494189 0.275373 0.190402 0 0.251244
learn 0.212028 0.500498 0.291114 0.194696 0 0.260372
perform 0 0 0 0 0 0
train 0.204653 0.494128 0.269468 0.186544 0 0.252847
use model dataset class learn perform \
test 0.742916 0.681393 0 0.681496 0.683699 0
covid 0.665757 0.724479 0 0.726887 0.717042 0
peopl 0.515411 0.522393 0 0.505246 0.532415 0
symptom 0.330993 0.256259 0 0.260397 0.262241 0
case 0.45149 0.420446 0 0.4196 0.416605 0
travel 0.29489 0.222238 0 0.227429 0.234838 0
fever 0.289428 0.204474 0 0.208538 0.212028 0
infect 0.571065 0.491545 0 0.494189 0.500498 0
contact 0.345758 0.268131 0 0.275373 0.291114 0
respiratori 0.276271 0.186408 0 0.190402 0.194696 0
imag 0 0 0 0 0 0
data 0.26882 0.252962 0 0.251244 0.260372 0
use 3.72529e-09 0.202653 0 0.205026 0.216197 0
model 0.202653 6.58545e-10 0 0.0528 0.0795498 0
dataset 0 0 0 0 0 0
class 0.205026 0.0528 0 0 0.0802578 0
learn 0.216197 0.0795498 0 0.0802578 0 0
perform 0 0 0 0 0 0
train 0.197632 0.0548807 0 0.0521263 0.0777509 0
train
test 0.680553
covid 0.722203
peopl 0.524164
symptom 0.256927
case 0.417977
travel 0.222096
fever 0.204653
infect 0.494128
contact 0.269468
respiratori 0.186544
imag 0
data 0.252847
use 0.197632
model 0.0548807
dataset 0
class 0.0521263
learn 0.0777509
perform 0
train 0
(-0.045145602008440484, 0.58879, -0.22155190658315219)
#loading dataset
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for word in words:
i=i+1
document = document+word+' '
if i == 200:
documents_list.append(document)
document,i = '',0
if len(documents_list) == 2:
break
print("Total Number of Documents:",len(documents_list))
#titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science.txt")
Number of words in text file : 120257 Total Number of Documents: 2
#data preprocessing
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'most', 'medical', 'centers', 'hence', 'many', 'cases', 'images', 'become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks', 'have', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'with', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'such', 'systems', 'they', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'most', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels', 'manuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date'] ['publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '2020', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '2015', '18136', 'gómez', 'ríos', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'data', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center']
# LSA Model
number_of_topics=2
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words)
#top 5 words from LSA
dict(model_science.show_topic(0, topn=5))
{'support': -0.30386095534296476,
'2020': -0.23522191481735744,
'granada': -0.23033778261060142,
'covid': -0.21722636684891058,
'programm': -0.2025739702286432}
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph8.png')
# Cosine similarity matrix
a=pd.DataFrame()
for i in range(number_of_topics):
words_covid_science =dict(model_science.show_topic(i, topn=words))
b=pd.DataFrame(words_covid_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled9.png')
cosine-similarity
support 2020 granada covid programm
support 1 0.524928 0.955931 -0.115612 1
2020 0.524928 1 0.751685 0.784751 0.524928
granada 0.955931 0.751685 1 0.181107 0.955931
covid -0.115612 0.784751 0.181107 1 -0.115612
programm 1 0.524928 0.955931 -0.115612 1
#networkx
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig9.png')
#data loading
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")
Number of words in text file : 8713 Total Number of Documents: 2
#data cleaning
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'reality', 'reuters', 'published', 'july', '2020', 'updated', 'july', '2020', 'marc', 'jones', 'london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes', 'have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return'] ['infection', 'rates', 'which', 'make', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june', 'euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases']
# LSA Model
number_of_topics=2
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values
#top 5 words
dict(model_nscience.show_topic(0, topn=5))
{'market': 0.264353040306379,
'rate': 0.18754847840846847,
'juli': 0.1791266417580859,
'covid': 0.17912664175808563,
'june': 0.17045279709658656}
#wordcloud of top 10 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph9.png')
# cosine similarity matrix for top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
b=pd.DataFrame(words_covid_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_nscience.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled10.png')
cosine-similarity
market rate juli covid june
market 1 0.711061 0.771166 0.771166 0.459512
rate 0.711061 1 0.10071 0.10071 0.951241
juli 0.771166 0.10071 1 1 -0.21108
covid 0.771166 0.10071 1 1 -0.21108
june 0.459512 0.951241 -0.21108 -0.21108 1
#networkx
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig10.png')
#data losding
document_list,titles,word_count_psy=load_data("","covid-psychology.txt")
Number of words in text file : 86550 Total Number of Documents: 2
#data preprocessing
clean_text=preprocess_data(document_list)
['international', 'sociology', '2021', 'author', '2020', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '1177', '0268580920948807', 'journals', 'sagepub', 'home', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid'] ['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated', 'data']
# LSA Model
number_of_topics=2
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words)
#dictionary of top 5 words
dict(model_psy.show_topic(0, topn=5))
{'misinfodem': 0.4936266655941646,
'covid': 0.3571916107745935,
'articl': 0.21002057347124545,
'mechan': 0.14717103730334843,
'health': 0.1471710373033483}
#wordcloud
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph28.png')
# Cosinesimilarity matrix for top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model_psy.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled11.png')
cosine-similarity
misinfodem covid articl mechan health
misinfodem 1 0.977431 0.994116 0.761706 0.761706
covid 0.977431 1 0.948797 0.881393 0.881393
articl 0.994116 0.948797 1 0.687043 0.687043
mechan 0.761706 0.881393 0.687043 1 1
health 0.761706 0.881393 0.687043 1 1
#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig11.png')
#mantel test for 10 word network
mantel_test(5)
Pairwise-distance
misinfodem covid articl mechan health support 2020 \
misinfodem 0 0 0 0 0 0 0
covid 0 0 0.360337 0 0 0.608033 0.282616
articl 0 0.360337 0 0 0 0.404283 0.208896
mechan 0 0 0 0 0 0 0
health 0 0 0 0 0 0 0
support 0 0.608033 0.404283 0 0 0 0.327071
2020 0 0.282616 0.208896 0 0 0.327071 0
granada 0 0.457677 0.252649 0 0 0.161988 0.175514
programm 0 0.526543 0.280864 0 0 0.126325 0.246469
granada programm
misinfodem 0 0
covid 0.457677 0.526543
articl 0.252649 0.280864
mechan 0 0
health 0 0
support 0.161988 0.126325
2020 0.175514 0.246469
granada 0 0.0742376
programm 0.0742376 0
Pairwise-distance
misinfodem covid articl mechan health support \
misinfodem 0 0.1894 0.293937 0.419015 0.419015 0
covid 0.1894 0 0.156809 0.234496 0.234496 0
articl 0.293937 0.156809 0 0.170443 0.170443 0
mechan 0.419015 0.234496 0.170443 0 0 0
health 0.419015 0.234496 0.170443 0 0 0
support 0 0 0 0 0 0
2020 0.468991 0.293937 0.1894 0.0784044 0.0784044 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
2020 granada programm
misinfodem 0.468991 0 0
covid 0.293937 0 0
articl 0.1894 0 0
mechan 0.0784044 0 0
health 0.0784044 0 0
support 0 0 0
2020 0 0 0
granada 0 0 0
programm 0 0 0
(-0.27891572318575647, 0.87877, -1.1456133484409994)
#mantel test for 20 word network
mantel_test(10)
Pairwise-distance
misinfodem covid articl mechan health commun psycholog \
misinfodem 0 0 0 0 0 0 0
covid 0 0 0.360337 0 0 0 0
articl 0 0.360337 0 0 0 0 0
mechan 0 0 0 0 0 0 0
health 0 0 0 0 0 0 0
commun 0 0 0 0 0 0 0
psycholog 0 0 0 0 0 0 0
strategi 0 0 0 0 0 0 0
misinform 0 0 0 0 0 0 0
polit 0 0 0 0 0 0 0
support 0 0.608033 0.404283 0 0 0 0
2020 0 0.282616 0.208896 0 0 0 0
granada 0 0.457677 0.252649 0 0 0 0
programm 0 0.526543 0.280864 0 0 0 0
research 0 0.526543 0.280864 0 0 0 0
tabik 0 0.421418 0.189487 0 0 0 0
garcía 0 0.318922 0.129222 0 0 0 0
project 0 0.492938 0.220392 0 0 0 0
decsai 0 0.492938 0.220392 0 0 0 0
strategi misinform polit support 2020 granada \
misinfodem 0 0 0 0 0 0
covid 0 0 0 0.608033 0.282616 0.457677
articl 0 0 0 0.404283 0.208896 0.252649
mechan 0 0 0 0 0 0
health 0 0 0 0 0 0
commun 0 0 0 0 0 0
psycholog 0 0 0 0 0 0
strategi 0 0 0 0 0 0
misinform 0 0 0 0 0 0
polit 0 0 0 0 0 0
support 0 0 0 0 0.327071 0.161988
2020 0 0 0 0.327071 0 0.175514
granada 0 0 0 0.161988 0.175514 0
programm 0 0 0 0.126325 0.246469 0.0742376
research 0 0 0 0.126325 0.246469 0.0742376
tabik 0 0 0 0.220392 0.148475 0.0631623
garcía 0 0 0 0.323975 0.0843519 0.161988
project 0 0 0 0.189487 0.222713 0.0843519
decsai 0 0 0 0.189487 0.222713 0.0843519
programm research tabik garcía project decsai
misinfodem 0 0 0 0 0 0
covid 0.526543 0.526543 0.421418 0.318922 0.492938 0.492938
articl 0.280864 0.280864 0.189487 0.129222 0.220392 0.220392
mechan 0 0 0 0 0 0
health 0 0 0 0 0 0
commun 0 0 0 0 0 0
psycholog 0 0 0 0 0 0
strategi 0 0 0 0 0 0
misinform 0 0 0 0 0 0
polit 0 0 0 0 0 0
support 0.126325 0.126325 0.220392 0.323975 0.189487 0.189487
2020 0.246469 0.246469 0.148475 0.0843519 0.222713 0.222713
granada 0.0742376 0.0742376 0.0631623 0.161988 0.0843519 0.0843519
programm 0 0 0.109024 0.218047 0.0631623 0.0631623
research 0 0 0.109024 0.218047 0.0631623 0.0631623
tabik 0.109024 0.109024 0 0.109024 0.0742376 0.0742376
garcía 0.218047 0.218047 0.109024 0 0.175514 0.175514
project 0.0631623 0.0631623 0.0742376 0.175514 0 0
decsai 0.0631623 0.0631623 0.0742376 0.175514 0 0
Pairwise-distance
misinfodem covid articl mechan health commun \
misinfodem 0 0.1894 0.293937 0.419015 0.419015 0.419015
covid 0.1894 0 0.156809 0.234496 0.234496 0.234496
articl 0.293937 0.156809 0 0.170443 0.170443 0.170443
mechan 0.419015 0.234496 0.170443 0 0 0
health 0.419015 0.234496 0.170443 0 0 0
commun 0.419015 0.234496 0.170443 0 0 0
psycholog 0.360699 0.235213 0.0784044 0.185806 0.185806 0.185806
strategi 0.360699 0.235213 0.0784044 0.185806 0.185806 0.185806
misinform 0.505066 0.315666 0.284614 0.115951 0.115951 0.115951
polit 0.409223 0.240466 0.126266 0.0762243 0.0762243 0.0762243
support 0 0 0 0 0 0
2020 0.468991 0.293937 0.1894 0.0784044 0.0784044 0.0784044
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0.529541 0.350635 0.252533 0.120233 0.120233 0.120233
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
psycholog strategi misinform polit support 2020 \
misinfodem 0.360699 0.360699 0.505066 0.409223 0 0.468991
covid 0.235213 0.235213 0.315666 0.240466 0 0.293937
articl 0.0784044 0.0784044 0.284614 0.126266 0 0.1894
mechan 0.185806 0.185806 0.115951 0.0762243 0 0.0784044
health 0.185806 0.185806 0.115951 0.0762243 0 0.0784044
commun 0.185806 0.185806 0.115951 0.0762243 0 0.0784044
psycholog 3.72529e-09 0 0.300211 0.115951 0 0.170443
strategi 0 0 0.300211 0.115951 0 0.170443
misinform 0.300211 0.300211 0 0.185806 0 0.152449
polit 0.115951 0.115951 0.185806 0 0 0.0631332
support 0 0 0 0 0 0
2020 0.170443 0.170443 0.152449 0.0631332 0 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0.229409 0.229409 0.14117 0.126266 0 0.0631332
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
granada programm research tabik garcía project decsai
misinfodem 0 0 0.529541 0 0 0 0
covid 0 0 0.350635 0 0 0 0
articl 0 0 0.252533 0 0 0 0
mechan 0 0 0.120233 0 0 0 0
health 0 0 0.120233 0 0 0 0
commun 0 0 0.120233 0 0 0 0
psycholog 0 0 0.229409 0 0 0 0
strategi 0 0 0.229409 0 0 0 0
misinform 0 0 0.14117 0 0 0 0
polit 0 0 0.126266 0 0 0 0
support 0 0 0 0 0 0 0
2020 0 0 0.0631332 0 0 0 0
granada 0 0 0 0 0 0 0
programm 0 0 0 0 0 0 0
research 0 0 0 0 0 0 0
tabik 0 0 0 0 0 0 0
garcía 0 0 0 0 0 0 0
project 0 0 0 0 0 0 0
decsai 0 0 0 0 0 0 0
(-0.16544931052577933, 0.8724, -1.1200916601203454)
mantel_test_n(5)
Pairwise-distance
market rate juli covid june support 2020 granada \
market 0 0 0 0 0 0 0 0
rate 0 0 0 0 0 0 0 0
juli 0 0 0 0 0 0 0 0
covid 0 0 0 0 0 0.608033 0.282616 0.457677
june 0 0 0 0 0 0 0 0
support 0 0 0 0.608033 0 0 0.327071 0.161988
2020 0 0 0 0.282616 0 0.327071 0 0.175514
granada 0 0 0 0.457677 0 0.161988 0.175514 0
programm 0 0 0 0.526543 0 0.126325 0.246469 0.0742376
programm
market 0
rate 0
juli 0
covid 0.526543
june 0
support 0.126325
2020 0.246469
granada 0.0742376
programm 0
Pairwise-distance
market rate juli covid june support \
market 0 0.197323 0.172313 0.172313 0.323988 0
rate 0.197323 0 0.331629 0.331629 0.129455 0
juli 0.172313 0.331629 0 0 0.459925 0
covid 0.172313 0.331629 0 0 0.459925 0
june 0.323988 0.129455 0.459925 0.459925 0 0
support 0 0 0 0 0 0
2020 0.174022 0.286306 0.0801315 0.0801315 0.409594 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
2020 granada programm
market 0.174022 0 0
rate 0.286306 0 0
juli 0.0801315 0 0
covid 0.0801315 0 0
june 0.409594 0 0
support 0 0 0
2020 0 0 0
granada 0 0 0
programm 0 0 0
(-0.3211636322477467, 0.97417, -1.4414045746819804)
mantel_test_n(10)
Pairwise-distance
market rate juli covid june also friday job infect million \
market 0 0 0 0 0 0 0 0 0 0
rate 0 0 0 0 0 0 0 0 0 0
juli 0 0 0 0 0 0 0 0 0 0
covid 0 0 0 0 0 0 0 0 0 0
june 0 0 0 0 0 0 0 0 0 0
also 0 0 0 0 0 0 0 0 0 0
friday 0 0 0 0 0 0 0 0 0 0
job 0 0 0 0 0 0 0 0 0 0
infect 0 0 0 0 0 0 0 0 0 0
million 0 0 0 0 0 0 0 0 0 0
support 0 0 0 0.608033 0 0 0 0 0 0
2020 0 0 0 0.282616 0 0 0 0 0 0
granada 0 0 0 0.457677 0 0 0 0 0 0
programm 0 0 0 0.526543 0 0 0 0 0 0
research 0 0 0 0.526543 0 0 0 0 0 0
tabik 0 0 0 0.421418 0 0 0 0 0 0
garcía 0 0 0 0.318922 0 0 0 0 0 0
project 0 0 0 0.492938 0 0 0 0 0 0
decsai 0 0 0 0.492938 0 0 0 0 0 0
support 2020 granada programm research tabik \
market 0 0 0 0 0 0
rate 0 0 0 0 0 0
juli 0 0 0 0 0 0
covid 0.608033 0.282616 0.457677 0.526543 0.526543 0.421418
june 0 0 0 0 0 0
also 0 0 0 0 0 0
friday 0 0 0 0 0 0
job 0 0 0 0 0 0
infect 0 0 0 0 0 0
million 0 0 0 0 0 0
support 0 0.327071 0.161988 0.126325 0.126325 0.220392
2020 0.327071 0 0.175514 0.246469 0.246469 0.148475
granada 0.161988 0.175514 0 0.0742376 0.0742376 0.0631623
programm 0.126325 0.246469 0.0742376 0 0 0.109024
research 0.126325 0.246469 0.0742376 0 0 0.109024
tabik 0.220392 0.148475 0.0631623 0.109024 0.109024 0
garcía 0.323975 0.0843519 0.161988 0.218047 0.218047 0.109024
project 0.189487 0.222713 0.0843519 0.0631623 0.0631623 0.0742376
decsai 0.189487 0.222713 0.0843519 0.0631623 0.0631623 0.0742376
garcía project decsai
market 0 0 0
rate 0 0 0
juli 0 0 0
covid 0.318922 0.492938 0.492938
june 0 0 0
also 0 0 0
friday 0 0 0
job 0 0 0
infect 0 0 0
million 0 0 0
support 0.323975 0.189487 0.189487
2020 0.0843519 0.222713 0.222713
granada 0.161988 0.0843519 0.0843519
programm 0.218047 0.0631623 0.0631623
research 0.218047 0.0631623 0.0631623
tabik 0.109024 0.0742376 0.0742376
garcía 0 0.175514 0.175514
project 0.175514 0 0
decsai 0.175514 0 0
Pairwise-distance
market rate juli covid june also \
market 0 0.197323 0.172313 0.172313 0.323988 0.104544
rate 0.197323 0 0.331629 0.331629 0.129455 0.204797
juli 0.172313 0.331629 0 0 0.459925 0.129455
covid 0.172313 0.331629 0 0 0.459925 0.129455
june 0.323988 0.129455 0.459925 0.459925 0 0.331629
also 0.104544 0.204797 0.129455 0.129455 0.331629 0
friday 0.104544 0.204797 0.129455 0.129455 0.331629 0
job 0.160263 0.0861564 0.258909 0.258909 0.204797 0.129455
infect 0.160263 0.0861564 0.258909 0.258909 0.204797 0.129455
million 0.271949 0.0801315 0.388364 0.388364 0.0861564 0.258909
support 0 0 0 0 0 0
2020 0.174022 0.286306 0.0801315 0.0801315 0.409594 0.0861564
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0 0 0 0 0 0
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
friday job infect million support 2020 \
market 0.104544 0.160263 0.160263 0.271949 0 0.174022
rate 0.204797 0.0861564 0.0861564 0.0801315 0 0.286306
juli 0.129455 0.258909 0.258909 0.388364 0 0.0801315
covid 0.129455 0.258909 0.258909 0.388364 0 0.0801315
june 0.331629 0.204797 0.204797 0.0861564 0 0.409594
also 0 0.129455 0.129455 0.258909 0 0.0861564
friday 0 0.129455 0.129455 0.258909 0 0.0861564
job 0.129455 0 0 0.129455 0 0.204797
infect 0.129455 0 0 0.129455 0 0.204797
million 0.258909 0.129455 0.129455 0 0 0.331629
support 0 0 0 0 0 0
2020 0.0861564 0.204797 0.204797 0.331629 0 0
granada 0 0 0 0 0 0
programm 0 0 0 0 0 0
research 0 0 0 0 0 0
tabik 0 0 0 0 0 0
garcía 0 0 0 0 0 0
project 0 0 0 0 0 0
decsai 0 0 0 0 0 0
granada programm research tabik garcía project decsai
market 0 0 0 0 0 0 0
rate 0 0 0 0 0 0 0
juli 0 0 0 0 0 0 0
covid 0 0 0 0 0 0 0
june 0 0 0 0 0 0 0
also 0 0 0 0 0 0 0
friday 0 0 0 0 0 0 0
job 0 0 0 0 0 0 0
infect 0 0 0 0 0 0 0
million 0 0 0 0 0 0 0
support 0 0 0 0 0 0 0
2020 0 0 0 0 0 0 0
granada 0 0 0 0 0 0 0
programm 0 0 0 0 0 0 0
research 0 0 0 0 0 0 0
tabik 0 0 0 0 0 0 0
garcía 0 0 0 0 0 0 0
project 0 0 0 0 0 0 0
decsai 0 0 0 0 0 0 0
(-0.24513328801204826, 0.99907, -1.8270394036391986)
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
if text !='':
documents_list.append(text)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count_science=load_data("","covid-science.txt")
Number of words in text file : 120257 Total Number of Documents: 14379
#data cleaning
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet'] ['methodology', 'predicting', 'covid'] ['based', 'chest', 'images'] ['tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte'] ['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova'] ['olmedo', 'sánchez', 'herrera'] ['abstract', 'currently', 'coronavirus', 'disease', 'covid'] ['most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans'] ['chest', 'images', 'computed', 'tomography'] ['scanners', 'testing', 'available', 'most'] ['medical', 'centers', 'hence', 'many', 'cases', 'images'] ['become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks'] ['have', 'great', 'potential', 'building', 'covid', 'triage', 'systems'] ['detecting', 'covid', 'patients', 'especially', 'patients', 'with'] ['severity', 'unfortunately', 'current', 'databases', 'allow'] ['building', 'such', 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[] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['kerala'] [] [] [] [] [] [] [] [] [] [] [] [] [] ['1208'] ['1807'] ['gujarat'] [] [] [] [] [] [] [] ['1272'] ['2848'] ['4948'] ['7404'] ['9931'] [] ['15953'] ['18584'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['delhi'] [] [] [] [] [] [] [] ['1707'] ['2501'] ['4068'] ['6261'] ['8895'] ['12319'] [] ['25004'] [] [] [] [] [] [] [] [] [] [] [] [] ['1056'] ['1743'] ['2728'] ['4329'] ['ladakh'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['1355'] ['1974'] ['2838'] ['3433'] ['4595'] ['6170'] ['7672'] ['8762'] [] [] [] [] [] [] [] ['1574'] ['3323'] ['7029'] ['11823'] ['19101'] ['29100'] ['44582'] ['62357'] ['77793'] [] [] [] [] [] [] [] [] [] [] ['1062'] ['1143'] ['1454'] ['1761'] ['2378'] ['3147'] ['table', 'mathematical', 'description', 'datasets'] ['week'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['count'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['mean'] [] [] [] [] [] [] [] ['1028'] ['1814'] ['2881'] 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'cumulative', 'case', 'data', 'were', 'converted', 'daily', 'case'] ['growth', 'rate', 'which', 'difference', 'between', 'logarithms'] ['cumulative', 'case', 'numbers', 'successive', 'days', 'these'] ['implementation', 'indicators', 'were', 'regressed', 'case', 'growth', 'rate'] ['using', 'panel', 'regression', 'analysis', 'panel', 'regression', 'widely'] ['used', 'analyze', 'dimensional', 'panel', 'data', 'which', 'typically'] ['cross', 'sectional', 'states', 'countries', 'longitudinal'] ['year', 'month', 'dimensions', 'specifically', 'fixed', 'effects'] ['panel', 'regression', 'model', 'adopted', 'study', 'could'] ['model', 'unobserved', 'heterogeneity', 'through', 'state', 'specific', 'fixed'] ['effects', 'addition', 'growth', 'rate', 'multiplied'] ['regression', 'thus', 'regression', 'coefficient', 'policy'] ['could', 'interpreted', 'percentage', 'point', 'changes', 'growth'] ['rate', 'figure'] ['figure', 'scraped', 'data', 'product', 'support', 'covid', 'policy'] ['analysis'] ['study', 'demonstrated', 'that', 'stay', 'home', 'orders'] ['workplace', 'closures', 'public', 'information', 'campaigns'] ['drastically', 'decrease', 'confirmed', 'case', 'growth', 'rate', 'stay', 'home', 'orders', 'workplace', 'closure', 'decrease', 'case', 'growth', 'rate'] ['through', 'changes', 'mobility', 'while', 'public', 'information', 'paign', 'impact', 'confirmed', 'case', 'growth', 'rate', 'through', 'channels'] ['other', 'than', 'mobility', 'addition', 'regarding', 'death', 'case', 'growth'] ['rate', 'stay', 'home', 'orders', 'international', 'travel', 'controls'] ['limited', 'mitigation', 'effect', 'relation', 'between', 'policies'] ['84794', 'volume', '2021h', 'covid', 'scraper', 'open', 'source', 'toolset'] ['case', 'growth', 'rates', 'learned', 'study', 'could', 'provide'] ['policymakers', 'better', 'understanding', 'effectiveness'] ['each', 'policy', 'support', 'decision', 'making'] ['conclusion'] ['covid', 'outbreak', 'impacted', 'billions', 'people', 'over'] ['world', 'governments', 'organizations', 'research', 'insti', 'tutions', 'conducting', 'rapid', 'research', 'covid', 'related'] ['problems', 'that', 'bring', 'people', 'every', 'country', 'back'] ['normalcy', 'detailed', 'spatiotemporal', 'covid', 'records', 'data'] ['proved', 'important', 'evidence', 'support', 'covid'] ['related', 'research', 'however', 'collect', 'aggregate', 'store'] ['share', 'data', 'published', 'each', 'country', 'world'] ['community', 'effectively', 'challenge', 'solve', 'this', 'prob', 'covid', 'scraper', 'developed', 'open', 'sourced'] ['toolset', 'that', 'automatically', 'scan', 'extract', 'collect', 'filter'] ['refine', 'unify', 'store', 'public', 'spatiotemporal', 'covid'] ['records', 'fifty', 'eight', 'countries', 'around', 'world', 'which'] ['provide', 'available', 'covid', 'data', 'sources', 'with', 'minor'] ['code', 'adjustments', 'this', 'toolset', 'accommodate', 'various', 'types'] ['data', 'published', 'each', 'country', 'various', 'data', 'formats'] ['scales', 'channels', 'publish', 'frequencies', 'more', 'importantly'] ['countries', 'that', 'provide', 'access', 'historical'] ['covid', 'data', 'automatically', 'build', 'historical', 'data', 'lections', 'support', 'research', 'repeatedly', 'certain', 'frequency'] ['covid', 'scraper', 'processes', 'high', 'effective', 'manner'] ['collecting', 'data', 'from', 'countries', 'over', 'world', 'within', 'single'] ['about', 'minutes', 'after', 'post', 'processing', 'data'] ['cleaning', 'fetched', 'data', 'unified', 'saved', 'into', 'database'] ['sharing', 'with', 'daily', 'data', 'quality', 'checking', 'data', 'product'] ['production', 'global', 'covid', 'data', 'github', 'repository'] ['been', 'maintained', 'since', 'march', '2020', 'addition', 'visual', 'ization', 'component', 'developed', 'covid', 'scraper'] ['publish', 'data', 'product', 'service', 'public', 'view'] ['access'] ['covid', 'scraper', 'utilized', 'scraping', 'technolo', 'gies', 'that', 'used', 'data', 'science', 'related', 'fields'] ['integrating', 'open', 'source', 'packages', 'tools', 'data'] ['extracting', 'network', 'simulation', 'file', 'image', 'parsing', 'work', 'flow', 'automation', 'covid', 'scraper', 'highly', 'flexible'] ['automatic', 'toolset', 'that', 'process', 'tasks', 'unsupervised', 'under'] ['users', 'settings', 'with', 'nature', 'open', 'source', 'users'] ['easily', 'customize', 'data', 'sources', 'data', 'structure'] ['output', 'data', 'product', 'execution', 'logic', 'processing', 'frequency'] ['exception', 'handling', 'addition', 'users', 'modify'] ['source', 'code', 'extend', 'collecting', 'datasets', 'other'] ['purposes', 'support', 'wider', 'studies', 'tasks', 'such', 'emer', 'gency', 'response', 'natural', 'disaster', 'detection', 'saving'] ['lives'] ['currently', 'limitation', 'that', 'data', 'quality', 'control'] ['validation', 'cannot', 'fully', 'automated', 'because', 'accuracy'] ['parsing', 'text', 'extracting', 'cannot', 'always', 'guaranteed'] ['using', 'current', 'packages', 'hence', 'users', 'need', 'intervene'] ['data', 'quality', 'control', 'process', 'image', 'type'] ['data', 'make', 'sure', 'data', 'product', 'high', 'quality', 'with'] ['rapid', 'development', 'text', 'parsing', 'from', 'images', 'will', 'keep'] ['updating', 'this', 'component', 'minimize', 'human', 'intervention'] ['automation', 'process']
# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)
#top 5 words from lsa
dict(model_science.show_topic(0, topn=5))
{'covid': 0.7824307537118228,
'imag': 0.3483697703449934,
'use': 0.19262982910237736,
'data': 0.16979352187609095,
'model': 0.1434213806427849}
#word cloud of top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph10.png')
# cosine similarity of top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model_science.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_science.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled12.png')
cosine-similarity
covid imag use data model
covid 1 0.00105585 -5.09149e-05 0.00048856 0.000807228
imag 0.00105585 1 -0.000564989 -0.00559808 0.0011847
use -5.09149e-05 -0.000564989 1 8.0517e-05 0.00060903
data 0.00048856 -0.00559808 8.0517e-05 1 -0.000274743
model 0.000807228 0.0011847 0.00060903 -0.000274743 1
#networkx of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig12.png')
#data loading
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")
Number of words in text file : 8713 Total Number of Documents: 387
#data preprocessing
clean_text=preprocess_data(document_list)
['covid', 'recovery', 'covid', 'reality'] ['reuters'] ['published', 'july', '2020', 'updated', 'july', '2020'] ['marc', 'jones'] ['london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves'] ['market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained'] ['chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise'] ['currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday'] ['think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes'] ['have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return', 'infection', 'rates', 'which', 'make', 'nervous'] ['against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june'] ['euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown'] ['futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence'] ['nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors'] ['economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures'] ['number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada'] ['recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume'] ['more', 'than', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'with', 'cases', 'florida', 'spiking', 'above'] ['nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'view', 'that', 'overall', 'situation', 'still', 'improving', 'overall'] ['year', 'german', 'government', 'bond', 'yields', 'basis', 'points', 'this', 'week', 'their', 'biggest', 'weekly', 'rise', 'month', 'though', 'they', 'nudged', 'down', 'friday', 'riskier', 'italian', 'yields', 'fell', 'well', 'though', 'which', 'their', 'lowest', 'since', 'late', 'march'] ['prices', 'also', 'eased', 'after', 'otherwise', 'solid', 'week', 'brent', 'crude', 'fell', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'both', 'were', 'around', 'this', 'time', 'months'] ['copper', 'prices', 'were', 'poised', 'seventh', 'consecutive', 'weekly', 'gain', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply'] ['three', 'month', 'copper', 'hovering', 'tonne', 'more', 'than', 'from', 'lows', 'ploughed', 'march'] ['issue', 'that', 'hangs', 'over', 'markets', 'will', 'surge', 'secondary', 'infections', 'that', 'will', 'trigger', 'second', 'wave', 'national', 'rather', 'than', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'note'] ['reporting', 'marc', 'jones'] ['newspaper'] ['covid', 'vaccine', 'frontrunners'] ['reuters'] ['published', 'october', '2020', 'updated', 'october', '2020'] ['reuters', 'pfizer', 'said', 'could', 'file', 'late', 'november', 'authorization', 'covid', 'vaccine', 'developing', 'opening', 'possibility', 'vaccine', 'being', 'available', 'united', 'states', 'year', 'moderna', 'astrazeneca', 'close', 'behind', 'largest', 'drugmaker', 'likely', 'have', 'early', 'data', 'their', 'vaccine', 'candidates', 'before', 'year', 'well', 'more', 'than', 'potential', 'vaccines', 'being', 'developed', 'tested', 'globally', 'stop', 'covid', 'pandemic', 'with', 'human', 'trials', 'according', 'world', 'health', 'organization', 'following', 'list', 'tracks', 'candidates', 'that', 'final', 'stages', 'testing', 'separate', 'factbox', 'coronavirus', 'vaccines', 'under', 'development', 'click', 'company', 'stage', 'development', 'expected', 'doses', 'pfizer', 'late', 'stage', 'trials', 'underway', 'option', 'biontech', 'pfizer', 'said', 'earlier', 'october', 'more', 'could', 'file', 'emergency', 'authorization', 'late', 'november', 'indicating', 'that', 'vaccine', 'could', 'potentially', 'available', 'additional', 'year', 'canada', 'specified', 'japan', 'moderna', 'completed', 'enrollment', 'option', 'subjects', 'late', 'stage', 'trial', 'additional', 'interim', 'results', 'from', 'trial', 'expected', 'november', 'japan', 'more', 'distributed', 'takeda', 'canada', 'specified', 'johnson', 'johnson', 'large', 'vaccine', 'trial', 'more', 'resume', 'after', 'paused', 'under', 'subsequent', 'agreement', 'illness', 'study', 'volunteer', 'results', 'from', 'trial', 'expected', 'option', '2020', 'early', '2021', 'more', 'additional', 'canada', 'japan', 'applicable', 'astrazeneca', 'late', 'stage', 'trials', 'underway', 'with', 'supply', 'deals', 'with', 'countries', 'oxford', 'university', 'trial', 'vaccine', 'candidate', 'more', 'than', 'doses', 'pause', 'since', 'sept', 'after', 'patient', 'fell', 'trial', 'results', 'expected', 'coming', 'weeks', 'part', 'commitment', 'deliver', 'total', 'least', 'additional', 'italy', 'germany', 'netherlands', 'france', 'additional', 'canada', 'japan', 'sinovac', 'biotech', 'late', 'stage', 'trials', 'brazil', 'vaccine', 'approved', 'emergency', 'indonesia', 'turkey', 'underway', 'china', 'july', 'supply', 'deal', 'prelim', 'data', 'expected', 'november', 'with', 'indonesia', 'least', 'doses', 'before', '2021', 'gamaleya', 'research', 'late', 'stage', 'trial', 'main', 'covid', 'people', 'inoculated', 'institute', 'vaccine', 'sputnik', 'underway', 'russia', 'supply', 'deals', 'with', 'since', 'sept', 'with', 'prelim', 'data', 'countries', 'including', 'india', 'expected', 'november', 'brazil', 'saudi', 'arabia', 'more', 'than', 'doses', 'cansino', 'biologics', 'vaccine', 'candidate', 'final', 'stage', 'mexico', 'doses', 'early', 'trials', 'been', 'approved', 'chinese', 'military', 'late', 'stage', 'trial', 'underway', 'pakistan', 'sinopharm', 'group', 'late', 'stage', 'trials', 'expects', 'produce', 'more', 'than', 'underway', 'billion', 'doses', '2021', 'reporting', 'dania', 'nadeem', 'mrinalika', 'bengaluru', 'editing', 'ankur', 'banerjee', 'maju', 'samuel'] ['newspaper'] ['giants', 'another', 'covid', 'case'] ['reuters'] ['published', 'november', '2020', 'updated', 'november', '2020'] ['york', 'giants', 'have', 'another', 'case', 'covid'] ['monday', 'night', 'were', 'notified', 'that', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'self', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'both', 'staff', 'members', 'were', 'identified', 'were', 'informed', 'remain', 'home', 'today'] ['giants', 'entering', 'their', 'week', 'play', 'week', 'this', 'second', 'time', 'team', 'player', 'test', 'positive', 'covid'] ['prior', 'playing', 'tampa', 'buccaneers', 'week', 'offensive', 'lineman', 'will', 'hernandez', 'tested', 'positive'] ['hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'list', 'returned', 'team', 'last', 'week', 'played', 'sunday', 'victory', 'over', 'philadelphia', 'eagles'] ['field', 'level', 'media'] ['newspaper'] ['covid', 'antivirals', 'taskforce', 'launched'] ['media'] ['published', 'april', '2021', 'updated', 'april', '2021'] ['home', 'covid', 'treatments', 'being', 'sought', 'team', 'government', 'experts', 'speed', 'recovery', 'time', 'people', 'become', 'infected', 'with', 'virus'] ['government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid'] ['prime', 'minister', 'boris', 'johnson', 'said', 'group', 'will', 'seek', 'medicines', 'stop', 'covid', 'tracks'] ['hoped', 'that', 'antivirals', 'could', 'help', 'reduce', 'infections', 'limit', 'impact', 'variants'] ['they', 'also', 'help', 'protect', 'people', 'cannot', 'take', 'vaccines', 'those', 'fully', 'protected', 'after', 'having'] ['officials', 'hope', 'find', 'bring', 'treatments', 'online', 'this', 'year'] ['this', 'could', 'mean', 'that', 'people', 'with', 'covid', 'those', 'they', 'have', 'been', 'close', 'contact', 'with', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'form', 'stop', 'infection', 'spreading', 'speed', 'recovery', 'time'] ['antivirals', 'taskforce', 'which', 'appoint', 'chair', 'will', 'search', 'most', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken', 'home'] ['taskforce', 'will', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'also', 'look', 'manufacturing', 'opportunities'] ['antiviral', 'drugs', 'type', 'medication', 'used', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses'] ['success', 'vaccination', 'programme', 'demonstrated', 'what', 'achieve', 'when', 'bring', 'together', 'brightest', 'minds', 'said', 'johnson'] ['antivirals', 'taskforce', 'will', 'seek', 'develop', 'innovative', 'treatments', 'take', 'home', 'stop', 'covid', 'tracks'] ['these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'save', 'more', 'lives'] ['health', 'secretary', 'matt', 'hancock', 'said', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'ones', 'from', 'this', 'terrible', 'virus'] ['modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'have', 'played', 'crucial', 'part', 'response', 'pandemic', 'bringing', 'together', 'team', 'that', 'will', 'supercharge', 'search', 'antiviral', 'treatments', 'roll', 'them', 'soon', 'autumn'] ['government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'said', 'speed', 'which', 'vaccines', 'therapeutics', 'such', 'dexamethasone', 'have', 'been', 'identified', 'deployed', 'against', 'covid', 'been', 'critical', 'pandemic', 'response'] ['antivirals', 'tablet', 'form', 'another', 'tool', 'response'] ['they', 'could', 'help', 'protect', 'those', 'protected', 'ineligible', 'vaccines'] ['they', 'could', 'also', 'another', 'layer', 'defence', 'face', 'variants', 'concern'] ['nikita', 'kanani', 'medical', 'director', 'primary', 'care', 'england', 'said', 'research', 'into', 'reality', 'record', 'speed', 'during', 'pandemic', 'this', 'taskforce', 'will', 'help', 'identify', 'roll', 'even', 'more', 'convenient', 'treatments', 'patients', 'with', 'covid'] ['commenting', 'announcement', 'penny', 'ward', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'said', 'antiviral', 'treatment', 'influenza', 'been', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'even', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines'] ['said', 'that', 'there', 'number', 'early', 'phase', 'trials', 'looking', 'oral', 'antiviral', 'medications'] ['newspaper'] ['african', 'covid', 'deaths'] [] ['published', 'february', '2021', 'updated', 'february', '2021'] ['africa', 'thursday', 'recorded', 'more', 'than', 'deaths', 'from', 'covid', 'grim', 'milestone', 'likely', 'understate', 'real', 'toll', 'continent', 'billion', 'people', 'battles', 'second', 'wave', 'infections'] ['countries', 'region', 'have', 'death', 'toll', 'from', 'reported', 'cases', 'according', 'tally'] ['continent', 'relatively', 'spared', 'pandemic', 'last', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april', '2020'] ['south', 'africa', 'worst', 'african', 'country', 'rolled', 'mass', 'testing', 'campaign', 'start', 'pandemic'] ['date', 'country', 'recorded', 'nearly', 'million', 'cases', 'more', 'than', 'deaths'] ['those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'only', 'reflect', 'fraction', 'actual', 'case', 'load', 'health', 'specialists'] ['cases', 'clearly', 'under', 'reported', 'because', 'poor', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'also', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health', 'told'] ['understaffed', 'health', 'facilities', 'lack', 'means', 'have', 'meant', 'many', 'african', 'countries', 'have', 'been', 'unable', 'mass', 'testing'] ['many', 'countries', 'have', 'mainly', 'tests', 'capitals', 'further', 'moves', 'away', 'from', 'urban', 'centres', 'less', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'from', 'doctors', 'without', 'borders'] ['disease', 'that', 'unnoticed', 'with', 'asymptomatic', 'patients', 'with', 'symptoms', 'that', 'confused', 'with', 'others', 'added'] ['covid', 'found', 'pawpaw'] ['zimbabwe', 'country', 'with', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'with', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains'] ['tanzania', 'stopped', 'testing', '2020', 'after', 'claiming', 'found', 'positive', 'covid', 'case', 'pawpaw', 'quail', 'even', 'goat', 'tanzanian', 'government', 'last', 'released', 'official', 'figures', 'april'] ['someone', 'told', 'year', 'that', 'continent', 'would', 'deaths', 'from', 'this', 'infection', 'probably', 'would', 'have', 'believed', 'john', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'told', 'reporters', 'thursday'] ['toll', 'africa', 'however', 'significantly', 'lower', 'than', 'europe', 'world', 'most', 'affected', 'region', 'with', 'deaths', 'recorded', 'other', 'regions', 'that', 'badly', 'latin', 'america', 'with', 'deaths', 'united', 'states', 'canada', 'deaths'] ['after', 'sharp', 'increase', 'january', 'africa', 'figures', 'have', 'fallen', 'sharply', 'past', 'weeks', 'over', 'last', 'seven', 'days', 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'patrons'] ['dancing', 'events', 'allowed', 'with', 'density', 'quota', 'person', 'four', 'square', 'metres', 'dancefloor', 'maximum', 'people'] ['nightclubs', 'back', 'with', 'same', 'density', 'quota'] ['there', 'will', 'requirement', 'seated', 'service', 'venues', 'required', 'electronic', 'record', 'keeping'] ['classes', 'limited', 'person', 'four', 'square', 'metres', 'allowed', 'elsewhere', 'when', 'staffed', 'when', 'staffed', 'limit', 'person', 'eight', 'square', 'metres', 'applies'] ['number', 'people', 'allowed', 'attend', 'weddings', 'funerals', 'indoor', 'religious', 'gatherings', 'will', 'scrapped', 'density', 'limit', 'person', 'square', 'metres', 'applies'] ['same', 'rules', 'apply', 'libraries', 'arcades', 'open', 'homes', 'outdoor', 'seated', 'entertainment', 'venues', 'like', 'zoos'] ['seated', 'entertainment', 'venues', 'like', 'cinemas', 'permitted', 'cent', 'capacity', 'with', 'maximum', '1000', 'people'] ['indoor', 'seated', 'entertainment', 'venues', 'like', 'galleries', 'allowed', 'reach', 'cent', 'capacity', '1000', 'people', 'density', 'limit', 'person', 'square', 'metres', 'applies'] ['amusement', 'parks', 'open', 'with', 'cent', 'venue', 'capacity', 'density', 'limit', 'person', 'four', 'square', 'metres', 'applies', 'indoors', 'square', 'metres', 'outdoors'] ['gaming', 'allowed', 'with', 'limit', 'person', 'four', 'square', 'metres', 'enforced', 'every', 'second', 'machine', 'turned'] ['person', 'four', 'square', 'metres', 'will', 'permitted', 'inside', 'brothels', 'strip', 'clubs', 'while', 'patrons', 'strip', 'clubs', 'been', 'scrapped', 'brothels', 'allowed', 'only', 'people'] ['newspaper'] ['covid', 'restrictions', 'across', 'europe'] ['reuters'] ['published', 'february', '2021', 'updated', 'february', '2021'] ['reuters', 'britain', 'whose', 'economy', 'been', 'among', 'hardest', 'europe', 'covid', 'pandemic', 'monday', 'announce', 'phased', 'roadmap', 'lockdown', 'aided', 'world', 'fastest', 'vaccine', 'rollouts'] ['announcement', 'parliament', '1530', 'expected', 'confirm', 'opening', 'schools', 'from', 'march', 'stipulate', 'more', 'gradual', 'easing', 'other', 'restrictions', 'notably', 'retailers', 'hospitality', 'venues'] ['following', 'snapshot', 'restrictions', 'place', 'europe', 'other', 'leading', 'economies'] ['germany', 'essential', 'stores', 'closed', 'until', 'least', 'march', 'restaurants', 'offer', 'meals', 'take', 'only', 'museums', 'gyms', 'cinemas', 'closed', 'hotels', 'open', 'only', 'business', 'travellers', 'schools', 'some', 'federal', 'states', 'partially', 'opening', 'from', 'companies', 'must', 'offer', 'staff', 'option', 'work', 'from', 'home', 'where', 'possible'] ['france', 'nationwide', 'curfew', 'place', 'between', 'hours', '1800', '0600', 'schools', 'shops', 'open', 'cafes', 'restaurants', 'bars', 'shut', 'along', 'with', 'theatres', 'cinemas', 'museums', 'galleries', 'date', 'been', 'opening', 'anyone', 'entering', 'france', 'must', 'produce', 'negative', 'covid', 'test', 'entering', 'france', 'from', 'outside', 'european', 'union', 'allowed', 'except', 'urgent', 'reasons', 'italy', 'country', 'divided', 'into', 'orange', 'yellow', 'white', 'zones', 'restrictions', 'vary', 'accordingly', 'present', 'seven', 'regions', 'provinces', 'orange', 'zone', 'rest', 'yellow', 'orange', 'zone', 'means', 'bars', 'restaurants', 'closed', 'people', 'cannot', 'leave', 'their', 'towns', 'residence', 'except', 'work', 'emergencies', 'schools', 'closed', 'these', 'regions', 'open', 'least', 'partly', 'yellow', 'zones', 'theatres', 'cinemas', 'gyms', 'closed', 'nationwide', 'travel', 'between', 'regions', 'subject', 'limits', 'spain', 'restrictions', 'vary', 'with', 'madrid', 'taking', 'relaxed', 'approach', 'allowing', 'customers', 'drink', 'inside', 'bars', 'restaurants', 'until', 'most', 'other', 'regions', 'much', 'stricter', 'some', 'have', 'started', 'ease', 'local', 'travel', 'restrictions', 'reopen', 'essential', 'businesses', 'infections', 'decline', 'though', 'curfews', 'remain', 'place', 'between', 'midnight', 'nationwide', 'schools', 'open', 'netherlands', 'schools', 'essential', 'stores', 'bars', 'restaurants', 'shut', 'curfew', 'from', 'been', 'place', 'since', 'expected', 'extended', 'elementary', 'schools', 'care', 'centres', 'were', 'reopened', 'hairdressers', 'high', 'schools', 'follow', 'next', 'week', 'those', 'arriving', 'netherlands', 'must', 'provide', 'negative', 'covid', 'test', 'results'] ['switzerland', 'government', 'proposed', 'allowing', 'first', 'easing', 'steps', 'from', 'march', 'when', 'shops', 'museums', 'libraries', 'reopen', 'pending', 'final', 'green', 'light', 'wednesday', 'private', 'outdoor', 'events', 'with', 'people', 'would', 'also', 'allowed', 'from', 'current', 'limit', 'five', 'schools', 'many', 'lifts', 'remain', 'open', 'restaurants', 'cultural', 'venues', 'closed', 'additional', 'easing', 'follow', 'from', 'april', 'infection', 'rates', 'allow'] ['poland', 'poland', 'reopened', 'slopes', 'well', 'hotels', 'cinemas', 'theatres', 'maximum', 'capacity', 'week', 'trial', 'shopping', 'centres', 'open', 'restaurants', 'only', 'allowed', 'serve', 'food', 'take', 'away', 'bars', 'closed', 'children', 'kindergartens', 'first', 'three', 'years', 'primary', 'school', 'attend', 'lessons', 'normal', 'older', 'children', 'study', 'remotely', 'sweden', 'businesses', 'schools', 'remain', 'largely', 'open', 'with', 'focus', 'primarily', 'voluntary', 'social', 'distancing', 'high', 'schools', 'students', 'aged', 'above', 'have', 'partly', 'moved', 'online', 'while', 'alcohol', 'sales', 'bars', 'restaurants', 'banned', 'after', 'public', 'gatherings', 'more', 'than', 'eight', 'people', 'largely', 'forbidden', 'various', 'restrictions', 'also', 'apply', 'foreign', 'nationals', 'entering', 'sweden', 'domestic', 'travel'] ['belgium', 'shops', 'hairdressers', 'swimming', 'pools', 'schools', 'open', 'although', 'secondary', 'students', 'only', 'school', 'half', 'time', 'beauty', 'parlours', 'reopen', 'from', 'march', 'cafes', 'restaurants', 'shut', 'essential', 'foreign', 'travel', 'banned', 'until', 'april', 'working', 'from', 'home', 'mandatory', 'where', 'possible'] ['austria', 'lockdown', 'eased', 'this', 'month', 'despite', 'high', 'infections', 'shops', 'hairdressers', 'open', 'schools', 'have', 'person', 'lessons', 'with', 'daily', 'testing', 'nighttime', 'curfew', 'place', 'there', 'restrictions', 'leaving', 'tyrol', 'province', 'because', 'outbreak', 'south', 'african', 'variant', 'easing', 'measures', 'planned', 'until', 'easter', 'earliest', 'hungary', 'curfew', 'effect', 'with', 'exceptions', 'essential', 'move', 'work', 'health', 'reasons', 'walking', 'close', 'home', 'restaurants', 'closed', 'except', 'takeout', 'delivery', 'large', 'public', 'gatherings', 'allowed', 'cultural', 'venues', 'closed', 'with', 'events', 'online', 'only', 'secondary', 'schools', 'universities', 'closed', 'elementary', 'schools', 'open', 'weekly', 'testing', 'mandated', 'healthcare', 'workers', 'teachers', 'reporting', 'reuters', 'bureaus', 'compiled', 'mark', 'john', 'editing', 'larry', 'king'] ['newspaper'] ['nears', 'covid', 'deaths'] [] ['published', 'february', '2021', 'updated', 'february', '2021'] ['united', 'states', 'brink', 'sunday', 'grim', 'milestone', 'covid', 'related', 'deaths', 'since', 'start', 'pandemic', 'nation', 'virus', 'expert', 'warned', 'semblance', 'normalcy', 'return', 'until', 'year'] ['signs', 'hope', 'were', 'emerging', 'rollout', 'vaccines', 'dropping', 'massive', 'winter', 'spike', 'infections', 'heavy', 'toll', 'continues', 'mount', 'nation', 'that', 'reported', 'most', 'fatalities', 'cases', 'world'] ['terrible', 'historic', 'haven', 'seen', 'anything', 'even', 'close', 'this', 'well', 'over', 'hundred', 'years', 'since', '1918', 'pandemic', 'influenza', 'anthony', 'fauci', 'chief', 'medical', 'advisor', 'president', 'biden', 'said', 'meet', 'press'] ['something', 'that', 'stunning', 'when', 'look', 'numbers', 'almost', 'unbelievable', 'true', 'added', 'toll', 'johns', 'hopkins', 'university', 'tracking', 'website', 'stood', 'some'] ['after', 'first', 'covid', 'death', 'announced', 'united', 'states', 'february', '2020', 'took', 'about', 'three', 'months', 'pass', 'mark', 'during', 'first', 'wave', 'that', 'york', 'particularly', 'hard'] ['outbreak', 'spread', 'across', 'country', 'pace', 'deaths', 'increased', 'with', 'toll', 'jumping', 'from', 'just', 'over', 'month', 'amid', 'spike', 'fueled', 'part', 'holiday', 'gatherings'] ['fauci', 'noted', 'that', 'number', 'daily', 'infections', 'steep', 'decline', 'after', 'peaking', 'january', 'added', 'normal', 'life', 'still', 'some'] ['think', 'have', 'significant', 'degree', 'normality', 'into', 'fall', 'winter', 'year', 'fauci', 'said', 'state', 'union'] ['damage', 'done', 'pandemic'] ['biden', 'said', 'last', 'week', 'program', 'deliver', 'vaccines', 'into', 'people', 'arms', 'complicated', 'already', 'intense', 'challenges', 'manufacturing', 'them', 'huge', 'quantities', 'speed'] ['there', 'never', 'ever', 'ever', 'been', 'logistical', 'challenge', 'consequential', 'what', 'trying', 'getting', 'done', 'said'] ['according', 'biden', 'goal', 'administering', 'million', 'shots', 'total', 'million', 'first', 'days', 'presidency', 'track', 'easily', 'surpassed', 'with', 'current', 'average', 'million', 'vaccinations'] ['with', 'death', 'toll', 'soon', 'pass', 'biden', 'said', 'want', 'give', 'firm', 'predictions', 'when', 'crisis', 'will', 'curbed'] ['however', 'said', 'that', 'million', 'doses', 'enough', 'provide', 'dose', 'regimen', 'most', 'country', 'were', 'expected', 'ready', 'july'] ['more', 'than', 'million', 'people', 'have', 'received', 'least', 'shot', 'vaccine', 'united', 'states', 'with', 'some', 'million', 'getting', 'full', 'doses'] ['freezing', 'weather', 'snow', 'storms', 'that', 'united', 'states', 'over', 'week', 'have', 'slowed', 'nation', 'vaccine', 'efforts', 'with', 'some', 'million', 'doses', 'delayed'] ['gotten', 'million', 'doses', 'project', 'that', 'middle', 'week', 'will', 'have', 'caught', 'said', 'fauci', 'meet', 'press', 'calling', 'temporary', 'setback'] ['though', 'concern', 'grown', 'around', 'variants', 'virus', 'especially', 'those', 'that', 'appear', 'spread', 'more', 'easily', 'render', 'current', 'shots', 'less', 'potent', 'fauci', 'sounded', 'hopeful', 'note', 'whether', 'another', 'surge', 'coming'] ['think', 'that', 'inevitable', 'told', 'vaccines', 'currently', 'distributing', 'moderna', 'pfizer', 'vaccines', 'work', 'very', 'well', 'against', 'variant'] ['america', 'might', 'past', 'worst', 'pandemic', 'damage', 'deep', 'painful'] ['illustrate', 'this', 'milestone', 'half', 'million', 'deaths', 'york', 'times', 'published', 'front', 'page', 'graphic', 'running', 'length', 'page', 'with', 'each', 'small', 'point', 'representing', 'dead', 'american'] ['bottom', 'column', 'which', 'represents', 'deaths', 'recent', 'months', 'particularly', 'dark', 'almost', 'uniformly', 'black'] ['newspaper'] ['covid', 'restrictions', 'easing'] ['australian', 'associated', 'press'] ['published', 'september', '2020', 'updated', 'september', '2020'] ['easing', 'covid', 'restrictions', 'around', 'school', 'community', 'life', 'lower', 'community', 'acquired', 'covid', 'case', 'numbers'] ['weddings'] ['people', 'official', 'wedding', 'party', 'take', 'dance', 'floor', 'where', 'previously', 'only', 'bride', 'groom', 'were', 'permitted'] ['schools'] ['interschool', 'activities', 'resume', 'saturday', 'with', 'covid', 'safety', 'plan'] ['school', 'sport', 'will', 'return', 'normal', 'next', 'term', 'parents', 'school', 'sites'] ['playing', 'wind', 'instruments', 'singing', 'chanting', 'permitted', 'with', 'strict', 'restrictions'] ['year', 'formals', 'graduations', 'permitted'] ['high', 'school', 'formals', 'permitted', 'after'] ['kindergarten', 'orientation', 'back'] ['year', 'transition', 'back'] ['excursions', 'camps', 'resume', 'with', 'strict', 'guidelines'] ['school', 'choirs', 'musical', 'ensembles', 'resume', 'long', 'players', 'keep', 'metre', 'distance', 'from', 'another', 'three', 'metres', 'players', 'reeded', 'woodwind', 'instruments'] ['community', 'languages', 'schools', 'program', 'will', 'resume', 'face', 'face', 'learning'] ['sport'] ['more', 'than', 'parent', 'attend', 'community', 'sporting', 'activities', 'physical', 'distancing', 'least', 'metres', 'maintained'] ['accommodation', 'facilities', 'overnight', 'event', 'organisers', 'must', 'implement', 'covid', 'safety', 'plan'] ['carpooling', 'should', 'continue', 'avoided'] ['newspaper'] ['zealand', 'launches', 'covid', 'diary'] ['australian', 'associated', 'press'] ['published', '2020', 'updated', '2020'] ['belatedly', 'cases', 'covid', 'ease', 'zealand', 'government', 'launched', 'covid', 'help', 'kiwis', 'track', 'their', 'movements'] ['prime', 'minister', 'jacinda', 'ardern', 'launched', 'wednesday', 'labelling', 'digital', 'diary'] ['differs', 'from', 'australian', 'version', 'linking', 'other', 'users'] ['instead', 'allows', 'kiwis', 'check', 'different', 'venues', 'such', 'cafes', 'restaurants', 'avoid', 'onerous', 'writing', 'down', 'contact', 'details', 'every', 'place', 'visited'] ['helps', 'users', 'when', 'they', 'about', 'keep', 'their', 'movements', 'ardern', 'said'] ['this', 'that', 'people', 'that', 'keeps', 'data', 'themselves', 'rather', 'than', 'adding', 'into', 'more', 'broader', 'repository', 'that', 'might', 'held', 'business'] ['this', 'stage', 'data', 'sent', 'government', 'authorities', 'from', 'though', 'that', 'functionality', 'will', 'added', 'update', 'next', 'month'] ['different', 'functionality', 'means', 'there', 'user', 'threshold', 'required', 'useful', 'still', 'plenty', 'kiwis', 'have', 'taken'] ['even', 'before', 'officially', 'launched', 'kiwis', 'almost', 'cent', 'population', 'downloaded'] ['wednesday', 'director', 'general', 'health', 'ashley', 'bloomfield', 'announced', 'there', 'were', 'cases', 'covid', 'zealand', 'fourth'] ['there', 'have', 'been', 'just', 'cases', 'past', 'week', 'overall', 'past', 'fortnight'] ['ever', 'decreasing', 'numbers', 'will', 'bars', 'nightclubs', 'open', 'first', 'time', 'since', 'march', 'thursday', 'provided', 'customers', 'seated', 'groups', 'under', 'receive', 'table', 'service'] ['ardern', 'will', 'further', 'review', 'caps', 'gathering', 'numbers', 'currently', 'except', 'funerals', 'which', 'monday', 'cabinet', 'meeting'] ['plan', 'regularly', 'adjust', 'settings', 'allow', 'more', 'activity', 'when', 'data', 'says', 'safe', 'ardern', 'said'] ['showing', 'well', 'virus', 'been', 'squashed', 'over', 'past', 'months', 'just', 'zealander', 'being', 'treated', 'hospital', 'covid', 'does', 'require', 'intensive', 'care'] ['newspaper'] ['records', 'covid', 'cases', 'deaths'] ['reuters'] ['published', 'june', '2021', 'updated', 'june', '2021'] ['london', 'june', 'reuters', 'britain', 'reported', 'daily', 'covid', 'cases', 'government', 'figures', 'showed', 'thursday', 'down', 'slightly', 'from', 'reported', 'before', 'which', 'highest', 'daily', 'total', 'since', 'late', 'february'] ['britain', 'also', 'reported', 'seven', 'further', 'deaths', 'within', 'days', 'positive', 'covid', 'test', 'from', 'wednesday', 'taking', 'total', 'death', 'toll', 'this', 'measure'] ['some', 'british', 'adult', 'population', 'have', 'received', 'doses', 'covid', 'vaccine', 'have', 'received', 'least', 'dose', 'reporting', 'david', 'milliken', 'editing', 'kate', 'holton'] ['newspaper'] ['drugs', 'wrongly', 'touted', 'covid', 'treatments'] ['australian', 'associated', 'press'] ['published', 'august', '2020', 'updated', 'august', '2020'] ['medicines', 'falsely', 'touted', 'covid', 'treatments'] ['hydroxychloroquine', 'used', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'some', 'studies', 'found', 'make', 'things', 'worse'] ['remdesivir', 'antiviral', 'medicine', 'that', 'help', 'some', 'people', 'hospital', 'with', 'severe', 'covid', 'recover', 'faster', 'does', 'help', 'people', 'with', 'milder', 'covid'] ['dexamethasone', 'reduces', 'inflammation', 'used', 'treat', 'asthma', 'arthritis', 'reduces', 'risk', 'dying', 'people', 'hospital', 'with', 'serious', 'covid', 'that', 'need', 'help', 'breathe', 'doesn', 'help', 'people', 'with', 'less', 'severe', 'covid', 'could', 'make', 'things', 'worse'] ['ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'none', 'found', 'effective', 'against', 'covid', 'outside', 'intensive', 'care'] ['source', 'medicinewise'] ['newspaper'] ['covid', 'schools', 'study', 'breakdown'] ['australian', 'associated', 'press'] ['published', 'april', '2020', 'updated', 'april', '2020'] ['what', 'national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'into', 'covid', 'schools', 'found'] ['total', 'students', 'staff', 'members', 'from', 'schools', 'were', 'from', 'march', 'april', '2020', 'diagnosed', 'with', 'covid'] ['people', 'nine', 'were', 'students', 'nine', 'were', 'staff', 'members'] ['those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'over', 'that', 'time', 'period'] ['close', 'contacts', 'just', 'people', 'caught', 'covid'] ['those', 'people', 'both', 'students', 'pass', 'disease', 'others'] ['students', 'passed', 'coronavirus', 'school', 'staff', 'members'] ['spread', 'covid', 'within', 'schools', 'thus', 'been', 'very', 'limited', 'virus', 'transmission', 'children', 'schools', 'less', 'than', 'that', 'influenza'] ['newspaper'] ['sydney', 'club', 'shut', 'repeat', 'covid', 'breaches'] ['australian', 'associated', 'press'] ['published', 'october', '2020', 'updated', 'october', '2020'] ['sydney', 'portugal', 'community', 'club', 'being', 'forced', 'shut', 'seven', 'days', 'after', 'failing', 'comply', 'with', 'covid', 'safe', 'regulations', 'even', 'after', 'three', 'warnings', '5000', 'fine'] ['liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'said', 'portugal', 'club', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches'] ['will', 'close', 'from', 'wednesday', 'week'] ['inspectors', 'visited', 'club', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'plan', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'said', 'tuesday'] ['since', 'closure', 'order', 'issued', 'however', 'some', 'things', 'club', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'than', 'while', 'guests', 'need', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines'] ['what', 'changed', 'having', 'robust', 'digital', 'check', 'processes', 'that', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'case', 'visited', 'venue', 'argeres', 'said'] ['service', 'free', 'covid', 'safe', 'check', 'code', 'businesses', 'registered', 'covid', 'safe', 'anyone', 'check', 'using', 'this', 'code', 'even', 'they', 'have', 'service', 'account'] ['inspectors', 'from', 'liquor', 'gaming', 'fair', 'trading', 'safework', 'have', 'conducted', '5605', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'four', 'businesses'] ['newspaper'] ['tests', 'underway', 'covid', 'treatment'] ['australian', 'associated', 'press'] ['published', 'august', '2020', 'updated', 'august', '2020'] ['australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'they', 'hope', 'will', 'provide', 'relief', 'those', 'infected', 'with', 'covid', 'limit', 'spread'] ['professor', 'david', 'morris', 'international', 'team', 'have', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'that', 'could', 'prevent', 'virus', 'moving', 'into', 'lungs', 'carriers', 'spreading', 'other', 'people'] ['trial', 'coronavirus', 'patients', 'being', 'established', 'with', 'melbourne', 'hospital', 'could', 'start', 'next', 'month'] ['bromac', 'been', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'that', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells'] ['core', 'agents', 'pineapple', 'stem', 'enzyme', 'tested', 'after', 'observed', 'that', 'pigs', 'eating', 'pineapples', 'were', 'resistant', 'particular', 'gastro', 'conditions'] ['taken', 'drug', 'development', 'more', 'than', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'with', 'covid', 'professor', 'david', 'morris', 'said', 'statement', 'monday'] ['results', 'show', 'drug', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'from', 'infecting', 'other', 'cells'] ['hope', 'results', 'will', 'show', 'treatment', 'confine', 'covid', 'nose', 'throat', 'prevent', 'lung', 'infection', 'stop', 'infected', 'patients', 'from', 'passing', 'live', 'virus', 'said'] ['hoped', 'infected', 'person', 'could', 'take', 'medication', 'first', 'signs', 'covid', 'stop', 'virus', 'tracks'] ['this', 'could', 'provide', 'very', 'safe', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'back', 'their', 'everyday', 'lives'] ['researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'work', 'tandem', 'treatment', 'prevention', 'measure']
# LSA Model
number_of_topics=100
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values
#top 5 words from lsa
dict(model_nscience.show_topic(0, topn=5))
{'trial': -0.42977519738264497,
'vaccin': -0.3531586004468163,
'stage': -0.3053952578874054,
'late': -0.2756054075105182,
'expect': -0.21725840938344546}
#word cloud for top 5 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph11.png')
# cosine similarity of top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_non_science =dict(model_nscience.show_topic(i, topn=words))
b=pd.DataFrame(words_non_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_nscience.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled13.png')
cosine-similarity
trial vaccin stage late expect
trial 1 0.0812451 0.884778 0.710032 0.642674
vaccin 0.0812451 1 0.127836 0.0928863 0.029285
stage 0.884778 0.127836 1 0.782065 0.659594
late 0.710032 0.0928863 0.782065 1 0.553493
expect 0.642674 0.029285 0.659594 0.553493 1
#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig13.png')
# data loading
document_list,titles,word_count_psy=load_data("","covid-psychology.txt")
Number of words in text file : 86550 Total Number of Documents: 9007
#data cleaning
clean_text=preprocess_data(document_list)
['international', 'sociology'] ['2021'] ['author', '2020'] ['article', 'reuse', 'guidelines'] ['sagepub', 'journals', 'permissions'] ['1177', '0268580920948807'] ['journals', 'sagepub', 'home'] ['psychology', 'politics'] ['covid', 'misinfodemics'] ['people'] ['believe', 'misinfodemics'] ['sonia', 'mukhtar'] ['university', 'management', 'technology', 'lahore', 'pakistan'] ['abstract'] ['misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse'] ['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate'] ['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects'] ['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier'] ['about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this'] ['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects'] ['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article'] ['shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product'] ['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive'] ['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations'] ['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications'] ['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health'] ['keywords'] ['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science'] ['mechanisms', 'misinfodemics'] ['this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding'] ['corresponding', 'author'] ['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore'] ['punjab', '54770', 'pakistan'] ['email', 'sonia', 'mukhtar12', 'gmail'] ['948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar'] ['research', 'article2020'] ['article'] ['international', 'sociology'] ['covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus'] ['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern'] ['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin'] ['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during'] ['lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication'] ['become', 'major', 'source', 'interaction', 'holman', '2020'] ['covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges'] ['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable'] ['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories'] ['claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science'] ['times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false'] ['context', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease'] ['mukhtar', '2020a', '2020'] ['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'have'] ['also', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated'] ['these', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took'] ['life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed'] ['drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'make'] ['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed'] ['anti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this'] ['also', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'health'] ['psychosocial', 'impacts', 'aguilera', '2020', 'rana', '2020a', '2020b', 'these', 'considerations', 'become', 'even', 'more', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe'] ['popular', 'opinion', 'spend', 'ever', 'more', 'time', 'social', 'media', 'people', 'trying'] ['make', 'sense', 'their', 'changed', 'lives', 'self', 'proclaimed', 'celebrities', 'self', 'identified', 'social'] ['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their'] ['subjective', 'interpretations', 'events', 'this', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', '2020'] ['scientific', 'studies', 'from', 'previous', 'disease', 'outbreaks', 'have', 'demonstrated', 'that', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', '2019', 'kalichman', '2009', 'individuals', 'endorse'] ['misinformation', 'about', 'disease', 'less', 'likely', 'follow', 'public', 'health', 'instructions', 'during'] ['ebola', 'outbreak', '2014', 'respondents', 'gave', 'credence', 'conspiracy', 'theories'] ['alleged', 'that', 'they', 'would', 'less', 'likely', 'seek', 'support', 'during', 'disease', 'outbreak'] ['national', 'cancer', 'institute', '2020', '2019', 'study', 'montanaro', '2020', 'conducted'] ['over', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', '2020', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information'] ['about', 'covid', 'from', 'current', 'administration', 'mainstream', 'media', 'news', 'outlets'] ['york', 'times', '2020'] ['mukhtar'] ['psychology', 'misinfodemics'] ['fear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual'] ['perceived', 'threat', 'mertens', '2018', 'however', 'when', 'fear', 'excessive', 'then', 'this'] ['have', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'like', 'anxiety'] ['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'when', 'fear'] ['insufficient', 'then', 'this', 'also', 'cause', 'harm', 'both', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation'] ['policies', 'that', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal'] ['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'fear', 'fear', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial'] ['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'help', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'have', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact'] ['efforts', 'curb', 'misinfodemics', 'process', 'have', 'increased', 'because', 'adverse'] ['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable'] ['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health'] ['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination'] ['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', '2018', 'propensity', 'follow', 'popular'] ['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety', 'fear', 'lead'] ['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'high', 'levels', 'uncertainty', 'inability', 'cope', 'with', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety'] ['fear', 'health', 'related', 'worry', 'this', 'situation', 'exacerbated', 'exposure', 'plethora'] ['information', 'including', 'misinformation', 'disinformation', 'about', 'impending'] ['threat', 'from', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', '2019', 'perceived', 'threat'] ['information', 'elevates', 'fear', 'repeated', 'engagement', 'with', 'trauma', 'related', 'media', 'content'] ['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently'] ['will', 'either', 'increase', 'fear', 'virus', 'cause', 'insensitivity', 'towards', 'course'] ['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter'] ['accuracy', 'content', 'become', 'more', 'challenging', 'increasing', 'number', 'people', 'rely'] ['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'they', 'emulsify', 'into', 'false'] ['misleading', 'click', 'bait', 'content', 'world', 'health', 'organization', 'launched'] ['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic'] ['news', 'social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher'] ['acute', 'stress', 'holman', '2020', 'media', 'exposure', 'accumulates', 'ever', 'emerging', 'threats'] ['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation'] ['herd', 'mentality', 'misplaced', 'conviction', 'make', 'people', 'more', 'vulnerable', 'media', 'exposure', 'distress'] ['alongside', 'amount', 'media', 'exposure', 'type', 'content', 'exposure', 'matters'] ['well', 'exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could'] ['instigate', 'posttraumatic', 'stress', 'fear', 'future', 'which', 'will', 'lead', 'poor', 'personal'] ['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media'] ['have', 'established', 'themselves', 'beyond', 'government', 'channel', 'global'] ['international', 'sociology'] ['population', 'developed', 'affinity', 'news', 'channels', 'particular', 'while', 'older'] ['population', 'mainly', 'relies', 'more', 'traditional', 'news', 'channels', 'youth', 'look', 'social', 'media'] ['such', 'tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media'] ['have', 'become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers'] ['attract', 'attention', 'create', 'panic', 'liang', '2020', 'some', 'self', 'proclaimed', 'celebrities', 'media', 'stars', 'have', 'become', 'influential', 'that', 'large', 'number', 'people', 'rely', 'solely'] ['their', 'content', 'merchant', '2020', 'some', 'social', 'media', 'posts', 'circulating', 'many'] ['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal'] ['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'this', 'traumatic', 'event', 'certain', 'posts', 'like', 'come'] ['this', 'quarantine', 'with', 'skill', 'your', 'side', 'hustle', 'started', 'more', 'knowledge', 'then'] ['never', 'lacked', 'time', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts'] ['prompting', 'people', 'utilize', 'their', 'time', 'learning', 'things', 'skills', 'exacerbate'] ['worry', 'already', 'anxiety', 'prone', 'individuals', 'place', 'psychological', 'pressure', 'productive', 'rather', 'messages', 'like', 'come', 'this', 'quarantine', 'with', 'skill'] ['your', 'side', 'hustle', 'started', 'more', 'knowledge', 'then', 'doing', 'just', 'fine', 'should', 'disseminated', 'social', 'media', 'individuals', 'realize', 'that', 'during', 'such', 'intense', 'traumatic', 'event', 'everyone', 'equally', 'endowed', 'with', 'strength', 'coping', 'strategies'] ['problem', 'solving', 'skills', 'transform', 'trauma', 'into', 'positive', 'emotion', 'which', 'okay'] ['source', 'shame', 'guilt'] ['there', 'coercive', 'impression', 'percolating', 'among', 'individuals', 'regarding', 'lockdown'] ['that', 'people', 'should', 'assume', 'holiday', 'that', 'they', 'must', 'utilize', 'their', 'time', 'productively', 'engage', 'occupational', 'academic', 'activities', 'this', 'psychological', 'pressure'] ['further', 'aggravated', 'feelings', 'guilt', 'shame', 'regret', 'sadness', 'self', 'pity', 'anger', 'internalized'] ['emotions', 'being', 'overwhelmed', 'negative', 'self', 'talk', 'unrealistic', 'expectations', 'perceived', 'sense', 'failure', 'mukhtar', '2020a', '2020b', 'psychological', 'pressure', 'compete', 'with', 'peers', 'achieving', 'maximum', 'tasks', 'producing', 'occupational', 'academic'] ['outcomes', 'harnessing', 'herd', 'followers', 'subscribers', 'media', 'accounts'] ['downplaying', 'trauma', 'that', 'others', 'will', 'have', 'devastating', 'effects', 'mental'] ['wellbeing'] ['lockdown', 'resulting', 'self', 'isolation', 'quarantine', 'social', 'distancing', 'removed'] ['from', 'normal', 'leisure', 'time', 'that', 'might', 'utilized', 'improved', 'personal', 'functioning'] ['collective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted', 'huge'] ['loss', 'lives', 'displacement', 'many', 'individuals', 'mukhtar', 'mukhtar', '2020', 'mukhtar'] ['rana', '2020', 'covid', 'individual', 'collective', 'traumatic', 'event', 'directly'] ['indirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towards'] ['minimizing', 'negative', 'effects', 'this', 'traumatic', 'covid', 'pandemic', 'survivors'] ['many', 'people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collective'] ['trauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d'] ['financial', 'burden', 'loneliness', 'emotional', 'behavioral', 'problems', 'grief', 'bereavement'] ['fear', 'losing', 'family', 'mental', 'health', 'issues', 'physical', 'injuries', 'fatalities'] ['isolated', 'people', 'facing', 'psychological', 'issues', 'require', 'trauma', 'focused', 'psychological'] ['support', 'mental', 'health', 'care', 'psychological', 'support', 'guidance', 'treatment', 'intervention'] ['information', 'such', 'psychological', 'cyber', 'counseling', 'smartphone'] ['mukhtar'] ['instance', 'mental', 'health', 'hotlines', 'professional', 'help', 'seeking', 'behaviors', 'should'] ['encouraged', 'endorsed', 'related', 'barriers', 'like', 'stigma', 'marginalization', 'discrimination', 'shaming', 'phobias', 'should', 'discouraged', 'through', 'government', 'driven', 'programs'] ['evidence', 'based', 'treatment', 'models'] ['emerging', 'pandemics', 'command', 'intellectual', 'incapacity', 'decision', 'making'] ['degree', 'irrationality', 'when', 'bombarded', 'with', 'conflicting', 'opinions', 'conspiracy', 'theories', 'thus', 'empirical', 'interpretation', 'origins', 'impacts', 'result', 'moral', 'judgments'] ['based', 'religious', 'cultural', 'beliefs', 'emergence', 'aids', 'garnered', 'plethora'] ['moral', 'judgments', 'orbiting', 'around', 'sexual', 'morality', 'substance', 'abuse', 'arrival'] ['sars', 'blame', 'directed', 'towards', 'primitive', 'farming', 'practices', 'guangzhou'] ['unprecedented', 'covid', 'pandemic', 'welcomed', 'magnitude', 'misinfodemics'] ['multitude', 'fronts', 'ophir', '2018', 'richtel', '2020', 'psychological', 'predisposition', 'reject'] ['authorized', 'information', 'through', 'denial', 'byproduct', 'deep', 'founded', 'mistrust'] ['tendency', 'view', 'major', 'social', 'political', 'events', 'through', 'conspiracies', 'partisan', 'motivations', 'byproduct', 'conspiracy', 'thinking', 'likely', 'explanatory', 'factors', 'understanding', 'believes', 'covid', 'misinfodemics', 'association', 'between'] ['conspiracy', 'thinking', 'defense', 'mechanism', 'denial', 'could', 'potential', 'reason'] ['refusal', 'corrective', 'actions', 'several', 'health', 'related', 'cases', 'carey', '2020'] ['misinfodemics', 'could', 'motivated', 'various', 'factors', 'epistemic', 'desire', 'causal'] ['explanation', 'subjective', 'certainty', 'existential', 'desire', 'control', 'security'] ['social', 'desire', 'maintain', 'positive', 'image', 'self', 'group', 'douglas', '2017'] ['stigmatization', 'labeling', 'scapegoating', 'fear', 'swiftly', 'follow', 'with', 'narrative', 'which'] ['invokes', 'vernacular', 'human', 'conflict', 'waging', 'against', 'killer', 'virus', 'indomitable', 'invisible', 'threat', 'enemy', 'armed', 'with', 'vaccine', 'flinging', 'victims', 'quarantine'] ['camps', 'losing', 'sense', 'proportion', 'ability', 'mitigate', 'fear', 'reasonable'] ['knowledge', 'based', 'measures', 'challenge', 'normalize', 'perceived', 'threat', 'infection'] ['mechanism', 'misinfodemics', 'engrossed', 'silos', 'public', 'information', 'which'] ['inept', 'limiting', 'risks', 'wald', '2008', 'instance', 'social', 'theme', 'focusing', 'socioeconomic', 'disruption', 'scientific', 'theme', 'focusing', 'medical', 'health', 'risk', 'communication', 'pandemic', 'theme', 'focusing', 'state', 'global', 'response', 'populations', 'most'] ['likely', 'affected', 'emerging', 'disease', 'pandemic', 'simultaneous', 'misinfodemics'] ['include', 'those', 'where', 'there', 'disproportionally', 'inadequate', 'health', 'literacy', 'disadvantaged', 'socioeconomic', 'groups', 'migrants', 'ethnic', 'minorities', 'vulnerable', 'groups'] ['including', 'older', 'people', 'people', 'with', 'chronic', 'health', 'conditions', 'people', 'with', 'disability'] ['mukhtar', '2020e', 'rowlands', '2015'] ['politics', 'coronavirus'] ['conspiracy', 'theories', 'heavily', 'influenced', 'geopolitics', 'have', 'spread', 'regarding', 'origin'] ['scale', 'prevention', 'treatment', 'self', 'diagnosis', 'disease', 'covid', 'being', 'viral'] ['bioweapon', 'genetically', 'engineered', 'rogue', 'government', 'with', 'racist', 'genocidal'] ['agenda', 'wage', 'economic', 'psychological', 'chinese', 'biological', 'weapon'] ['conspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 'kingdom'] ['india', 'ukraine', 'biological', 'weapon', 'conspiracy', 'theory', 'spread', 'throughout'] ['russia', 'iran', 'china', 'philippines', 'venezuela', 'plot', 'muslims', 'plot', 'jews'] ['international', 'sociology'] ['espionage', 'population', 'control', 'scheme', 'medical', 'misinformation', 'vaccine', 'preexistence', 'cocaine', 'cure', 'african', 'resistance', 'vegetarian', 'immunity', 'methanol'] ['other', 'views', 'endorsed', 'presidents', 'governments', 'public', 'figures', 'abound', 'causing'] ['misinfodemics', 'incorrect', 'information', 'about', 'virus', 'which', 'poses', 'risks'] ['global', 'scale', 'mccarthy', '2020'] ['meanwhile', 'covid', 'could', 'positively', 'viewed', 'through', 'partisan', 'lens'] ['right', 'wing', 'nationalism', 'pandemonium', 'allegations', 'coronavirus', 'gift'] ['politics', 'misinfodemics', 'censorship', 'pathological', 'nationalism', 'multitude'] ['governments', 'have', 'introduced', 'policy', 'blame', 'shifting', 'launching', 'their', 'powerrivalry', 'statements', 'revolving', 'around', 'illogical', 'reasoning', 'narrative', 'national'] ['immunity', 'towards', 'covid', 'there', 'claims', 'biological', 'weapon'] ['against', 'western', 'world', 'chinese', 'experiment', 'gone', 'wrong', 'china', 'equivalent'] ['ussr', 'level', 'threat', 'most', 'infamous', 'chinese', 'virus', 'italy', 'accused', 'migrants'] ['from', 'africa', 'disease', 'carriers', 'shores', 'france', 'hungary', 'there'] ['narration', 'correlation', 'between', 'immigrants', 'coronavirus', 'reminiscent'] ['influx', 'migrants', 'from', 'inflicted', 'countries', '2015', 'called', 'border'] ['crisis', 'european', 'right', 'wingers', 'declare', 'enemy', 'changed', 'migrants'] ['convergence', 'crises', 'aggravates', 'mistrust', 'scientific', 'political'] ['economic', 'bodies', 'around', 'world', 'these', 'narratives', 'imply', 'through', 'heavily', 'nuanced'] ['differential', 'discourse', 'that', 'their', 'countries', 'immune', 'coronavirus', 'they', 'would'] ['have', 'contracted', 'covid', 'could', 'manage', 'effectively', 'only', 'absence'] ['immigrants', 'mccarthy', '2020'] ['covid', 'provided', 'rich', 'fodder', 'europe', 'nativist', 'populist', 'tune', 'further'] ['incite', 'clamor', 'immigrants', 'building', 'walls', 'closing', 'borders', 'anti', 'immigration', 'policies', 'public', 'opinions', 'shaped', 'landscape', 'media', 'scientific', 'bodies'] ['frameworks', 'governments', 'thus', 'such', 'obstinate', 'prejudice', 'confrontations'] ['ignite', 'globalization', 'nationalism', 'nativism', 'protectionism', 'tariffs', 'closed', 'borders'] ['erection', 'walls', 'which', 'will', 'intensify', 'covid', 'pandemic', 'outbreak'] ['pakistan', 'research', 'study', 'conducted', 'ipsos', 'revealed', 'that', 'people'] ['country', 'believed', 'that', 'performing', 'ablution', 'will', 'keep', 'them', 'protected', 'from', 'transmitting'] ['coronavirus', 'others', 'believed', 'that', 'congregation', 'prayers', 'shaking', 'hands', 'cannot'] ['infect', 'anyone', 'since', 'sunnah', 'samaa', '2020', 'claims', 'circulating', 'about', 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['1nsrt'] [] [] [] ['1usc'] [] [] [] [] [] ['1usc'] [] [] [] [] [] ['1usd'] [] [] [] [] [] [] ['γ1nsrt', 'γ2ausc'] ['γ2busc'] [] [] ['γ3busd'] [] ['1ibert'] [] [] [] ['1ibert'] [] [] [] ['1spc'] [] [] [] [] [] ['1spc'] [] [] [] [] [] ['1spd'] [] [] [] [] [] [] ['γ1ibert', 'γ2aspc'] ['γ2bspc'] [] [] ['γ3bspd'] [] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] ['itld'] ['γ1ftmrt', 'γ2aitlc'] [] ['itlc'] [] ['itld'] [] ['itld'] [] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] ['1gerd'] [] [] [] [] [] ['gerd'] ['γ1daxrt', 'γ2agerc'] ['γ2bgerc'] [] ['gerd'] ['γ3bgerd'] [] ['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] ['1ukc'] [] [] [] [] [] ['1ukc'] [] [] [] [] [] ['1ukd'] [] [] [] [] [] [] ['γ1lsert', 'γ2aukc'] ['γ2bukc'] [] [] ['γ3bukd'] [] ['here', 'ϑ0ϑ1', 'ϑ2bϑ3aand', 'short', 'factors', 'while'] [] [] ['indicates', 'long', 'parameters'] ['whereas', 'symbolizes', 'number', 'used', 'model'] ['based', 'akaik', 'information', 'criterion', 'schwarz', 'information'] ['criterion', 'above', 'stated', 'equations', 'assumed', 'that'] ['confirmed', 'number', 'cases', 'deaths', 'covid', 'have'] ['asymmetrical', 'impact', 'stock', 'returns'] ['thus'] [] [] [] [] [] [] [] ['itlc'] [] [] ['itld'] [] ['gerc'] [] ['gerd'] [] [] [] [] [] ['designates', 'positive', 'shock', 'though'] [] [] [] [] [] [] [] [] ['itlc'] [] ['itld'] [] ['gerc'] [] ['gerd'] [] [] [] [] [] [] ['implies', 'negative', 'shock', 'each', 'variable'] ['computed', 'follows'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['here'] [] ['delineates', 'positive', 'shock', 'variable', 'denotes'] ['asymmetric', 'distributive', 'error', 'correction', 'form'] ['these', 'factors', 'written', 'follows'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] ['1usc'] [] [] [] [] [] ['1usc'] [] [] [] [] [] ['1usd'] [] [] [] [] [] [] ['φ1ectt'] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] ['1usc'] [] [] [] [] [] ['1usc'] [] [] [] [] [] ['1usd'] [] [] [] [] [] [] ['φ1ectt'] ['1ibert'] [] [] [] ['1ibert'] [] [] [] ['1spc'] [] [] [] [] [] ['1spc'] [] [] [] [] [] ['1spd'] [] [] [] [] [] [] ['φ1ectt'] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] ['itld'] ['φ1ectt'] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerd'] [] [] [] [] [] ['gerd'] ['φ1ectt'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] ['1ukc'] [] [] [] [] [] ['1ukc'] [] [] [] [] [] ['1ukd'] [] [] [] [] [] [] ['φ1ectt'] ['here', 'ectt', 'directs', 'error', 'correction', 'term', 'long'] ['cointegration', 'among', 'variables', 'examined', 'through', 'bound'] ['test', 'approach', 'pesaran', '2001', 'this', 'method', 'relies', 'test', 'evaluate', 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'lozano', 'rojas', 'simon', 'evidence', 'from', 'internet', 'search', 'data'] ['shows', 'information', 'seeking', 'responses', 'news', 'local', 'covid', 'cases', 'proceedings', 'national', 'academy'] ['sciences', '2020', 'epidemic', 'psychology', 'works', 'twitter'] ['bessi', 'ferrara', 'social', 'bots', 'distort', '2016', 'presidential', 'election', 'online', 'discussion', 'first', 'monday'] ['2016'] ['blau', 'exchange', 'power', 'social', 'life', 'transaction', 'publishers', '1964'] ['bleidorn', 'klimstra', 'denissen', 'rentfrow', 'potter', 'gosling', 'personality', 'maturation'] ['around', 'world', 'cross', 'cultural', 'examination', 'social', 'investment', 'theory', 'psychological', 'science', '2530'] ['2540', '2013'] ['borelli', 'sbarra', 'trauma', 'history', 'linguistic', 'self', 'focus', 'moderate', 'course', 'psychological'] ['adjustment', 'divorce', 'journal', 'social', 'clinical', 'psychology', '2011'] ['braun', 'clarke', 'using', 'thematic', 'analysis', 'psychology', 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'varga', 'quercia', 'social', 'dimensions', 'conversations', 'relationships'] ['proceedings', 'conference', '2020'] ['cinelli', 'quattrociocchi', 'galeazzi', 'valensise', 'brugnoli', 'schmidt', 'zola', 'zollo'] ['scala', 'covid', 'social', 'media', 'infodemic', 'arxiv', 'preprint', 'arxiv', '2003', '05004', '2020'] ['coppersmith', 'dredze', 'harman', 'quantifying', 'mental', 'health', 'signals', 'twitter', 'proceedings'] ['workshop', 'computational', 'linguistics', 'clinical', 'psychology', 'from', 'linguistic', 'signal', 'clinical', 'reality', 'pages'] ['2014'] ['schiano', 'covid', 'hangover', 'rising', 'tide', 'alcohol', 'disorder', 'alcohol', 'associated'] ['liver', 'disease', 'hepatology', '2020'] ['deri', 'rappaz', 'aiello', 'quercia', 'coloring', 'links', 'capturing', 'social', 'ties', 'they', 'perceived'] ['proceedings', 'conference', 'computer', 'supported', 'cooperative', 'work', 'social', 'computing', 'cscw'] ['pages', '2018'] ['deutsch', 'trust', 'suspicion', 'journal', 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'2009'] ['graham', 'haidt', 'koleva', 'motyl', 'iyer', 'wojcik', 'ditto', 'moral', 'foundations', 'theory'] ['pragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages'] ['elsevier', '2013'] ['grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses'] ['governors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', 'ssrn', '3578695', '2020'] ['hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'bias', 'dietary', 'self', 'report'] ['compromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology', '1995'] ['hochreiter', 'schmidhuber', 'long', 'short', 'term', 'memory', 'neural', 'computation', '1735', '1780', '1997'] ['hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'mind', 'volume'] ['mcgraw', 'hill', 'york', '2005'] ['holmes', 'connor', 'perry', 'tracey', 'wessely', 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'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases'] ['2020'] ['jimeno', 'yepes', 'mackinlay', 'chen', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter'] ['studies', 'health', 'technology', 'informatics', '2015'] ['johnson', 'fendrich', 'modeling', 'sources', 'self', 'report', 'bias', 'survey', 'drug', 'epidemiology', 'annals'] ['epidemiology', '2005'] ['kahn', 'tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'with', 'linguistic'] ['inquiry', 'word', 'count', 'american', 'journal', 'psychology', 'pages', '2007'] ['khan', 'huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer', '2019'] ['kornfield', 'toma', 'shah', 'moon', 'gustafson', 'what', 'before', 'relapse'] ['language', 'peer', 'peer', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'health'] ['communication', '1184', '1193', '2018'] ['kouzy', 'jaoude', 'kraitem', 'alam', 'karam', 'adib', 'zarka', 'traboulsi'] ['baddour', 'coronavirus', 'goes', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus'] ['2020'] ['ubler', 'ross', 'wessler', 'avioli', 'death', 'dying', 'jama', '1972'] ['goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr'] ['cartography', 'geographic', 'information', 'science', '2013'] ['wang', 'zhao', 'impact', 'covid', 'epidemic', 'declaration', 'psychological'] ['consequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health'] ['2032', '2020'] ['liang', 'effect', 'covid', 'youth', 'mental', 'health'] ['psychiatric', 'quarterly', 'pages', '2020'] ['goyal', 'joshi', 'chen', 'levy', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta'] ['robustly', 'optimized', 'bert', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '1907', '11692', '2019'] ['utkepohl', 'introduction', 'multiple', 'time', 'series', 'analysis', 'springer', 'science', 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'alani', 'alleviating', 'data', 'sparsity', 'twitter', 'sentiment', 'analysis', 'ceur', 'workshop', 'proceedings'] ['ceur', '2012'] ['salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto'] ['khandelwal', 'mabry', 'digital', 'epidemiology', 'plos', 'comput', 'biol', 'e1002616', '2012'] ['scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'from', 'social', 'media'] ['proceedings', 'conference', 'health', 'inference', 'learning', 'pages', '2020'] ['schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'life', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press', '1973', 'isbn'] ['9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c'] ['shaw', 'gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason'] ['lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'with', 'breast'] ['cancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer'] ['2007'] ['shen', 'rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'from', 'linguistic', 'signal', 'clinical', 'reality', 'pages', '2017'] ['shultz', 'baingana', 'neria', '2014', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response', 'jama', '2015'] ['simms', 'ramstedt', 'rich', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortions'] ['through', 'machine', 'learning', 'text', 'analytics', '2017', 'ieee', 'international', 'conference', 'healthcare', 'informatics', 'ichi'] ['pages', 'ieee', '2017'] ['smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association'] ['2012'] ['sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'fake', 'news', 'exploring'] ['health', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'case', 'study', 'american', 'journal'] ['health', 'education', '2018'] ['strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness', '1990'] ['tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizational'] ['identity', '1979'] ['tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'liwc', 'computerized', 'text', 'analysis'] ['methods', 'journal', 'language', 'social', 'psychology', '2010', 'epidemic', 'psychology', 'works', 'twitter'] ['economist', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international', '2020'] ['with', 'millions', 'stuck', 'home', 'online', 'wellness', 'industry', 'booming'] ['bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett', 'crum'] ['douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response'] ['nature', 'human', 'behaviour', 'pages', '2020'] ['varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection'] ['estimation', 'characterization', 'eleventh', 'international', 'aaai', 'conference', 'social', 'media', '2017'] ['wang', 'immediate', 'psychological', 'responses', 'associated'] ['factors', 'during', 'initial', 'stage', '2019', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'population'] ['china', 'international', 'journal', 'environmental', 'research', 'public', 'health', '1729', '2020'] ['waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion'] ['comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society', '1813', '1831', '2018'] ['windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises'] ['risk', 'hazards', 'crisis', 'public', 'policy', '2014'] ['wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center', '2019'] ['wolf', 'theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'online'] ['activity', 'journal', 'language', 'social', 'psychology', '2013'] ['wood', 'propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', '2015', '2016', 'zika', 'virus', 'outbreak'] ['cyberpsychology', 'behavior', 'social', 'networking', '2018'] ['yang', 'torres', 'lugo', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during'] ['covid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '2004', '14484', '2020'] ['acknowledgments'] ['thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'this', 'research'] ['partly', 'supported', 'grant', 'green', 'routes', '869764'] ['data', 'availability'] ['daily', 'aggregates', 'measurements', 'available', 'https', '6084', 'figshare', '14892642'] ['tweet', 'used', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasets'] ['visualizations', 'available', 'project', 'site', 'http', 'social', 'dynamics', 'epidemicpsychology'] ['competing', 'interests'] ['author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twitter'] ['supplementary', 'materials'] ['spatial', 'representativeness', 'twitter', 'data', 'make', 'sure', 'that', 'geographical', 'penetration', 'twitter'] ['data', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'user', 'activity', 'each', 'state', 'with'] ['census', 'population', 'figure', 'shows', 'that', 'both', 'number', 'twitter', 'users', 'volume', 'their', 'tweets'] ['correlate', 'very', 'strongly', 'with', 'census', 'population', 'population', 'estimates', 'year', '2019', 'level', 'states'] ['https', 'census'] [] ['population'] [] [] [] [] [] ['tweets'] [] [] [] [] [] ['population'] [] [] [] [] [] [] ['users'] [] [] [] [] ['figure', 'correlation', 'census', 'population', 'states', 'estimated', 'year', '2019', 'with'] ['number', 'tweets', 'posted', 'those', 'states', 'left', 'number', 'unique', 'twitter', 'users', 'posted'] ['least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'with'] ['coefficient', 'determination', 'linear', 'regression'] ['pearson', 'correlation', 'coefficient'] ['coefficient', 'number', 'tweets', 'that', 'twitter', 'users', 'scale', 'almost', 'linearly'] ['with', 'population', 'estimates'] ['signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'that', 'first', 'phase'] ['characterized', 'discussions', 'that', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones'] ['measured', 'volume', 'tweets', 'containing', 'hashtags', 'that', 'were', 'clear', 'indicators', 'content', 'that', 'either', 'racist'] ['aimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'from', 'list', 'hashtags', 'that', 'appeared'] ['least', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'over', 'time', 'they'] ['peak', 'during', 'first', 'phase', 'when', 'fear', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asian'] ['foreigners', 'were', 'their', 'peaks'] ['breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'into', 'regions', 'that'] ['were', 'dealing', 'with', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assess'] ['whether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed'] ['measures', 'that', 'represent', 'relative', 'presence', 'three', 'phases', 'over', 'time', 'refusal', 'anger', 'acceptance'] ['each', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curves'] ['indicate', 'that', 'three', 'phases', 'took', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states'] ['refusal', 'followed', 'anger', 'which', 'then', 'followed', 'acceptance', 'there', 'very', 'little', 'variation'] ['times', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']
# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values
#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)
#top 5 words from lsa
dict(model_psy.show_topic(0, topn=5))
{'2020': 0.744760590312811,
'covid': 0.2746753833089446,
'pandem': 0.22338134342717259,
'health': 0.20627927347916214,
'polici': 0.15875976238455333}
#word cloud for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph29.png')
# cosine similarity of top 5 words
a=pd.DataFrame()
for i in range(number_of_topics):
words_psychology =dict(model_psy.show_topic(i, topn=words))
b=pd.DataFrame(words_psychology,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model_psy.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled14.png')
cosine-similarity
2020 covid pandem health polici
2020 1 -5.71895e-05 0.000716471 0.000112255 -0.00409684
covid -5.71895e-05 1 0.00019057 -0.00189745 0.00221843
pandem 0.000716471 0.00019057 1 -0.00179825 -0.0134542
health 0.000112255 -0.00189745 -0.00179825 1 -0.00486343
polici -0.00409684 0.00221843 -0.0134542 -0.00486343 1
#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig14.png')
mantel_test(5)
Pairwise-distance
2020 covid pandem health polici imag \
2020 0 1.37262 1.03537 1.28802 0.951978 1.36784
covid 1.37262 1.49012e-08 1.07174 1.32865 0.995743 1.40464
pandem 1.03537 1.07174 0 0.950417 0.452413 1.09894
health 1.28802 1.32865 0.950417 2.10734e-08 0.879383 1.3261
polici 0.951978 0.995743 0.452413 0.879383 0 0.997534
imag 1.36784 1.40464 1.09894 1.3261 0.997534 0
use 1.37058 1.40725 1.09596 1.32584 0.9984 1.40638
data 1.36572 1.40266 1.08629 1.32313 0.987681 1.40569
model 1.37009 1.40533 1.09521 1.32659 0.998399 1.40384
use data model
2020 1.37058 1.36572 1.37009
covid 1.40725 1.40266 1.40533
pandem 1.09596 1.08629 1.09521
health 1.32584 1.32313 1.32659
polici 0.9984 0.987681 0.998399
imag 1.40638 1.40569 1.40384
use 0 1.40355 1.40608
data 1.40355 0 1.40248
model 1.40608 1.40248 0
Pairwise-distance
2020 covid pandem health polici imag \
2020 0 1.40842 1.40475 1.39349 1.19192 0.997544
covid 1.40842 0 1.4034 1.39314 1.18645 0.995691
pandem 1.40475 1.4034 0 1.38992 1.19124 0.99091
health 1.39349 1.39314 1.38992 2.58096e-08 1.17268 0.975351
polici 1.19192 1.18645 1.19124 1.17268 1.82501e-08 0.651343
imag 0.997544 0.995691 0.99091 0.975351 0.651343 0
use 1.37027 1.36815 1.36417 1.35124 1.1333 0.940915
data 1.36714 1.36384 1.36214 1.34878 1.1427 0.936453
model 1.33811 1.3362 1.33299 1.31859 1.1196 0.892189
use data model
2020 1.37027 1.36714 1.33811
covid 1.36815 1.36384 1.3362
pandem 1.36417 1.36214 1.33299
health 1.35124 1.34878 1.31859
polici 1.1333 1.1427 1.1196
imag 0.940915 0.936453 0.892189
use 0 1.30905 1.27562
data 1.30905 2.58096e-08 1.29622
model 1.27562 1.29622 2.10734e-08
(0.08137012686728233, 0.3607, 0.23074039777307215)
mantel_test(10)
Pairwise-distance
2020 covid pandem health polici psycholog \
2020 0 1.37262 1.03537 1.28802 0.951978 0.94286
covid 1.37262 1.49012e-08 1.07174 1.32865 0.995743 0.994685
pandem 1.03537 1.07174 0 0.950417 0.452413 0.459164
health 1.28802 1.32865 0.950417 2.10734e-08 0.879383 0.876935
polici 0.951978 0.995743 0.452413 0.879383 0 0.0870882
psycholog 0.94286 0.994685 0.459164 0.876935 0.0870882 0
impact 0.948046 0.994606 0.462294 0.841503 0.116237 0.0916567
countri 0.998861 1.06166 0.495437 0.914789 0.373444 0.359153
develop 1.07845 1.12368 0.672038 1.05522 0.500555 0.514655
nation 0.974168 1.02459 0.508374 0.847533 0.256235 0.229322
imag 1.36784 1.40464 1.09894 1.3261 0.997534 0.992911
use 1.37058 1.40725 1.09596 1.32584 0.9984 0.995574
data 1.36572 1.40266 1.08629 1.32313 0.987681 0.989576
model 1.37009 1.40533 1.09521 1.32659 0.998399 0.993666
detect 1.34284 1.3742 1.07082 1.29934 0.960186 0.955046
dataset 1.36678 1.40384 1.09729 1.32913 0.997587 0.992406
learn 1.34649 1.38262 1.05439 1.30164 0.96464 0.958069
pneumonia 1.32241 1.35689 1.06773 1.26718 0.925534 0.922983
case 1.36624 1.40235 1.09547 1.3214 0.985204 0.988768
impact countri develop nation imag use \
2020 0.948046 0.998861 1.07845 0.974168 1.36784 1.37058
covid 0.994606 1.06166 1.12368 1.02459 1.40464 1.40725
pandem 0.462294 0.495437 0.672038 0.508374 1.09894 1.09596
health 0.841503 0.914789 1.05522 0.847533 1.3261 1.32584
polici 0.116237 0.373444 0.500555 0.256235 0.997534 0.9984
psycholog 0.0916567 0.359153 0.514655 0.229322 0.992911 0.995574
impact 1.86265e-09 0.355451 0.499189 0.223964 0.999495 1.00256
countri 0.355451 7.45058e-09 0.508593 0.440892 1.05917 1.06014
develop 0.499189 0.508593 0 0.518995 1.11847 1.11817
nation 0.223964 0.440892 0.518995 0 1.01615 1.02501
imag 0.999495 1.05917 1.11847 1.01615 0 1.40638
use 1.00256 1.06014 1.11817 1.02501 1.40638 0
data 0.991414 1.03388 1.12068 1.01515 1.40569 1.40355
model 0.999246 1.06051 1.11272 1.02071 1.40384 1.40608
detect 0.959072 1.01322 1.07797 0.988459 1.38148 1.37941
dataset 0.996151 1.05761 1.11354 1.01716 1.40572 1.40489
learn 0.95205 1.02136 1.07576 0.989117 1.38078 1.3846
pneumonia 0.93538 0.99512 1.0447 0.947633 1.35547 1.35593
case 0.9921 1.03082 1.10603 1.02182 1.40123 1.40252
data model detect dataset learn \
2020 1.36572 1.37009 1.34284 1.36678 1.34649
covid 1.40266 1.40533 1.3742 1.40384 1.38262
pandem 1.08629 1.09521 1.07082 1.09729 1.05439
health 1.32313 1.32659 1.29934 1.32913 1.30164
polici 0.987681 0.998399 0.960186 0.997587 0.96464
psycholog 0.989576 0.993666 0.955046 0.992406 0.958069
impact 0.991414 0.999246 0.959072 0.996151 0.95205
countri 1.03388 1.06051 1.01322 1.05761 1.02136
develop 1.12068 1.11272 1.07797 1.11354 1.07576
nation 1.01515 1.02071 0.988459 1.01716 0.989117
imag 1.40569 1.40384 1.38148 1.40572 1.38078
use 1.40355 1.40608 1.37941 1.40489 1.3846
data 0 1.40248 1.37493 1.40314 1.37738
model 1.40248 0 1.37725 1.40469 1.37937
detect 1.37493 1.37725 2.58096e-08 1.37947 1.35634
dataset 1.40314 1.40469 1.37947 2.98023e-08 1.37978
learn 1.37738 1.37937 1.35634 1.37978 2.10734e-08
pneumonia 1.35335 1.35669 1.3213 1.35549 1.32988
case 1.39979 1.40221 1.37359 1.40191 1.37818
pneumonia case
2020 1.32241 1.36624
covid 1.35689 1.40235
pandem 1.06773 1.09547
health 1.26718 1.3214
polici 0.925534 0.985204
psycholog 0.922983 0.988768
impact 0.93538 0.9921
countri 0.99512 1.03082
develop 1.0447 1.10603
nation 0.947633 1.02182
imag 1.35547 1.40123
use 1.35593 1.40252
data 1.35335 1.39979
model 1.35669 1.40221
detect 1.3213 1.37359
dataset 1.35549 1.40191
learn 1.32988 1.37818
pneumonia 0 1.35097
case 1.35097 0
Pairwise-distance
2020 covid pandem health polici \
2020 0 1.40842 1.40475 1.39349 1.19192
covid 1.40842 0 1.4034 1.39314 1.18645
pandem 1.40475 1.4034 0 1.38992 1.19124
health 1.39349 1.39314 1.38992 2.58096e-08 1.17268
polici 1.19192 1.18645 1.19124 1.17268 1.82501e-08
psycholog 1.40268 1.39913 1.3974 1.38387 1.18285
impact 1.39118 1.38492 1.38301 1.37381 1.1802
countri 1.39477 1.39324 1.39042 1.37963 1.17909
develop 1.33854 1.33543 1.33842 1.32065 1.10161
nation 1.08424 1.07596 1.09128 1.0778 0.54018
imag 0.997544 0.995691 0.99091 0.975351 0.651343
use 1.37027 1.36815 1.36417 1.35124 1.1333
data 1.36714 1.36384 1.36214 1.34878 1.1427
model 1.33811 1.3362 1.33299 1.31859 1.1196
detect 0.998654 0.997841 0.992416 0.976091 0.653425
dataset 0.997911 0.994124 0.991011 0.974497 0.64985
learn 1.00684 1.00405 0.997215 0.987265 0.64682
pneumonia 0.997606 0.995377 0.990899 0.974354 0.651499
case 1.37294 1.36693 1.36747 1.35521 1.14194
psycholog impact countri develop nation imag \
2020 1.40268 1.39118 1.39477 1.33854 1.08424 0.997544
covid 1.39913 1.38492 1.39324 1.33543 1.07596 0.995691
pandem 1.3974 1.38301 1.39042 1.33842 1.09128 0.99091
health 1.38387 1.37381 1.37963 1.32065 1.0778 0.975351
polici 1.18285 1.1802 1.17909 1.10161 0.54018 0.651343
psycholog 2.98023e-08 1.3793 1.38459 1.33477 1.08156 0.985905
impact 1.3793 0 1.37429 1.32755 1.07049 0.971331
countri 1.38459 1.37429 0 1.31263 1.07942 0.975373
develop 1.33477 1.32755 1.31263 0 0.905358 0.891687
nation 1.08156 1.07049 1.07942 0.905358 7.45058e-09 0.435323
imag 0.985905 0.971331 0.975373 0.891687 0.435323 0
use 1.36368 1.35025 1.35629 1.29097 1.045 0.940915
data 1.36227 1.35031 1.35378 1.30716 1.01324 0.936453
model 1.32548 1.31874 1.32279 1.26 0.994455 0.892189
detect 0.983811 0.970599 0.966762 0.901796 0.440316 0.0698509
dataset 0.985898 0.969204 0.976041 0.890047 0.429926 0.0466436
learn 1.00011 0.990023 0.987257 0.903536 0.432831 0.162065
pneumonia 0.985137 0.969793 0.976123 0.891667 0.431109 0.0402169
case 1.3649 1.35457 1.35379 1.29358 1.04611 0.94385
use data model detect dataset learn \
2020 1.37027 1.36714 1.33811 0.998654 0.997911 1.00684
covid 1.36815 1.36384 1.3362 0.997841 0.994124 1.00405
pandem 1.36417 1.36214 1.33299 0.992416 0.991011 0.997215
health 1.35124 1.34878 1.31859 0.976091 0.974497 0.987265
polici 1.1333 1.1427 1.1196 0.653425 0.64985 0.64682
psycholog 1.36368 1.36227 1.32548 0.983811 0.985898 1.00011
impact 1.35025 1.35031 1.31874 0.970599 0.969204 0.990023
countri 1.35629 1.35378 1.32279 0.966762 0.976041 0.987257
develop 1.29097 1.30716 1.26 0.901796 0.890047 0.903536
nation 1.045 1.01324 0.994455 0.440316 0.429926 0.432831
imag 0.940915 0.936453 0.892189 0.0698509 0.0466436 0.162065
use 0 1.30905 1.27562 0.944919 0.935694 0.90979
data 1.30905 2.58096e-08 1.29622 0.919664 0.928952 0.962355
model 1.27562 1.29622 2.10734e-08 0.901443 0.894936 0.911068
detect 0.944919 0.919664 0.901443 0 0.0681092 0.170806
dataset 0.935694 0.928952 0.894936 0.0681092 0 0.159519
learn 0.90979 0.962355 0.911068 0.170806 0.159519 0
pneumonia 0.939319 0.936539 0.893902 0.0682208 0.0408703 0.153328
case 1.34192 1.32836 1.29484 0.945642 0.94495 0.954944
pneumonia case
2020 0.997606 1.37294
covid 0.995377 1.36693
pandem 0.990899 1.36747
health 0.974354 1.35521
polici 0.651499 1.14194
psycholog 0.985137 1.3649
impact 0.969793 1.35457
countri 0.976123 1.35379
develop 0.891667 1.29358
nation 0.431109 1.04611
imag 0.0402169 0.94385
use 0.939319 1.34192
data 0.936539 1.32836
model 0.893902 1.29484
detect 0.0682208 0.945642
dataset 0.0408703 0.94495
learn 0.153328 0.954944
pneumonia 0 0.938609
case 0.938609 0
(-0.2274986076411022, 0.83863, -1.0285543569883562)
mantel_test_n(5)
Pairwise-distance
trial vaccin stage late expect covid \
trial 3.29272e-10 0.334496 0.0652737 0.0457902 0.0703069 0.994227
vaccin 0.334496 0 0.334689 0.337998 0.340189 1.04304
stage 0.0652737 0.334689 0 0.0777504 0.0996353 0.994919
late 0.0457902 0.337998 0.0777504 0 0.0815712 0.995989
expect 0.0703069 0.340189 0.0996353 0.0815712 0 0.997507
covid 0.994227 1.04304 0.994919 0.995989 0.997507 1.49012e-08
imag 0.993552 1.04936 0.99207 0.992168 0.997729 1.40464
use 0.995028 1.05179 0.996482 0.996607 1.00076 1.40725
data 0.990223 1.04285 0.990314 0.990646 0.990675 1.40266
model 0.994301 1.04815 0.993383 0.993458 0.992835 1.40533
imag use data model
trial 0.993552 0.995028 0.990223 0.994301
vaccin 1.04936 1.05179 1.04285 1.04815
stage 0.99207 0.996482 0.990314 0.993383
late 0.992168 0.996607 0.990646 0.993458
expect 0.997729 1.00076 0.990675 0.992835
covid 1.40464 1.40725 1.40266 1.40533
imag 0 1.40638 1.40569 1.40384
use 1.40638 0 1.40355 1.40608
data 1.40569 1.40355 0 1.40248
model 1.40384 1.40608 1.40248 0
Pairwise-distance
trial vaccin stage late expect covid imag \
trial 0 0.945434 0.262596 0.366843 0.399192 1.08631 0
vaccin 0.945434 0 0.8639 0.879678 0.885463 1.24608 0
stage 0.262596 0.8639 7.45058e-09 0.242172 0.285506 1.01181 0
late 0.366843 0.879678 0.242172 0 0.332784 1.00813 0
expect 0.399192 0.885463 0.285506 0.332784 0 1.00751 0
covid 1.08631 1.24608 1.01181 1.00813 1.00751 0 0
imag 0 0 0 0 0 0 0
use 0.746335 1.00057 0.642882 0.645935 0.624019 1.07927 0
data 0.603135 0.912835 0.450508 0.526539 0.492882 1.05223 0
model 0.551106 0.824359 0.403968 0.407541 0.367668 0.964775 0
use data model
trial 0.746335 0.603135 0.551106
vaccin 1.00057 0.912835 0.824359
stage 0.642882 0.450508 0.403968
late 0.645935 0.526539 0.407541
expect 0.624019 0.492882 0.367668
covid 1.07927 1.05223 0.964775
imag 0 0 0
use 0 0.591809 0.548085
data 0.591809 0 0.445673
model 0.548085 0.445673 2.63418e-09
(0.01692738224462807, 0.49391, 0.057919303838280166)
mantel_test_n(10)
Pairwise-distance
trial vaccin stage late expect dose \
trial 3.29272e-10 0.334496 0.0652737 0.0457902 0.0703069 0
vaccin 0.334496 0 0.334689 0.337998 0.340189 0
stage 0.0652737 0.334689 0 0.0777504 0.0996353 0
late 0.0457902 0.337998 0.0777504 0 0.0815712 0
expect 0.0703069 0.340189 0.0996353 0.0815712 0 0
dose 0 0 0 0 0 0
underway 0 0 0 0 0 0
novemb 0.0455113 0.329619 0.0726449 0.0536986 0.0863373 0
addit 0.148744 0.34895 0.159562 0.144459 0.15068 0
develop 0.513383 0.43076 0.528358 0.506117 0.512406 0
covid 0.994227 1.04304 0.994919 0.995989 0.997507 0
imag 0.993552 1.04936 0.99207 0.992168 0.997729 0
use 0.995028 1.05179 0.996482 0.996607 1.00076 0
data 0.990223 1.04285 0.990314 0.990646 0.990675 0
model 0.994301 1.04815 0.993383 0.993458 0.992835 0
detect 0.955299 1.00736 0.961046 0.95723 0.959009 0
dataset 0.99098 1.04995 0.997247 0.992893 0.9881 0
learn 0.958264 1.02483 0.953309 0.958989 0.962703 0
pneumonia 0.923127 0.984885 0.929755 0.918661 0.925974 0
case 0.988888 1.04339 0.989346 0.990395 0.993517 0
underway novemb addit develop covid imag \
trial 0 0.0455113 0.148744 0.513383 0.994227 0.993552
vaccin 0 0.329619 0.34895 0.43076 1.04304 1.04936
stage 0 0.0726449 0.159562 0.528358 0.994919 0.99207
late 0 0.0536986 0.144459 0.506117 0.995989 0.992168
expect 0 0.0863373 0.15068 0.512406 0.997507 0.997729
dose 0 0 0 0 0 0
underway 0 0 0 0 0 0
novemb 0 0 0.156552 0.511979 0.995846 0.994225
addit 0 0.156552 2.63418e-09 0.494355 1.00624 1.00479
develop 0 0.511979 0.494355 0 1.12368 1.11847
covid 0 0.995846 1.00624 1.12368 1.49012e-08 1.40464
imag 0 0.994225 1.00479 1.11847 1.40464 0
use 0 0.996138 1.00759 1.11817 1.40725 1.40638
data 0 0.990878 0.995469 1.12068 1.40266 1.40569
model 0 0.994174 1.00565 1.11272 1.40533 1.40384
detect 0 0.957544 0.97319 1.07797 1.3742 1.38148
dataset 0 0.993604 1.00563 1.11354 1.40384 1.40572
learn 0 0.958197 0.975356 1.07576 1.38262 1.38078
pneumonia 0 0.924444 0.936387 1.0447 1.35689 1.35547
case 0 0.990169 1.0044 1.10603 1.40235 1.40123
use data model detect dataset \
trial 0.995028 0.990223 0.994301 0.955299 0.99098
vaccin 1.05179 1.04285 1.04815 1.00736 1.04995
stage 0.996482 0.990314 0.993383 0.961046 0.997247
late 0.996607 0.990646 0.993458 0.95723 0.992893
expect 1.00076 0.990675 0.992835 0.959009 0.9881
dose 0 0 0 0 0
underway 0 0 0 0 0
novemb 0.996138 0.990878 0.994174 0.957544 0.993604
addit 1.00759 0.995469 1.00565 0.97319 1.00563
develop 1.11817 1.12068 1.11272 1.07797 1.11354
covid 1.40725 1.40266 1.40533 1.3742 1.40384
imag 1.40638 1.40569 1.40384 1.38148 1.40572
use 0 1.40355 1.40608 1.37941 1.40489
data 1.40355 0 1.40248 1.37493 1.40314
model 1.40608 1.40248 0 1.37725 1.40469
detect 1.37941 1.37493 1.37725 2.58096e-08 1.37947
dataset 1.40489 1.40314 1.40469 1.37947 2.98023e-08
learn 1.3846 1.37738 1.37937 1.35634 1.37978
pneumonia 1.35593 1.35335 1.35669 1.3213 1.35549
case 1.40252 1.39979 1.40221 1.37359 1.40191
learn pneumonia case
trial 0.958264 0.923127 0.988888
vaccin 1.02483 0.984885 1.04339
stage 0.953309 0.929755 0.989346
late 0.958989 0.918661 0.990395
expect 0.962703 0.925974 0.993517
dose 0 0 0
underway 0 0 0
novemb 0.958197 0.924444 0.990169
addit 0.975356 0.936387 1.0044
develop 1.07576 1.0447 1.10603
covid 1.38262 1.35689 1.40235
imag 1.38078 1.35547 1.40123
use 1.3846 1.35593 1.40252
data 1.37738 1.35335 1.39979
model 1.37937 1.35669 1.40221
detect 1.35634 1.3213 1.37359
dataset 1.37978 1.35549 1.40191
learn 2.10734e-08 1.32988 1.37818
pneumonia 1.32988 0 1.35097
case 1.37818 1.35097 0
Pairwise-distance
trial vaccin stage late expect dose \
trial 0 0.945434 0.262596 0.366843 0.399192 0.748852
vaccin 0.945434 0 0.8639 0.879678 0.885463 0.992053
stage 0.262596 0.8639 7.45058e-09 0.242172 0.285506 0.66449
late 0.366843 0.879678 0.242172 0 0.332784 0.680024
expect 0.399192 0.885463 0.285506 0.332784 0 0.573659
dose 0.748852 0.992053 0.66449 0.680024 0.573659 1.05367e-08
underway 0.403056 0.859493 0.244095 0.297631 0.289229 0.632046
novemb 0.397539 0.849486 0.248758 0.287544 0.274898 0.639533
addit 0.445364 0.860979 0.310649 0.350592 0.337981 0.63689
develop 0.681659 0.980093 0.633853 0.652365 0.634082 0.847946
covid 1.08631 1.24608 1.01181 1.00813 1.00751 1.11255
imag 0 0 0 0 0 0
use 0.746335 1.00057 0.642882 0.645935 0.624019 0.806973
data 0.603135 0.912835 0.450508 0.526539 0.492882 0.734571
model 0.551106 0.824359 0.403968 0.407541 0.367668 0.63727
detect 0.559059 0.856794 0.398969 0.409769 0.366499 0.628541
dataset 0 0 0 0 0 0
learn 0.525798 0.835347 0.374226 0.376633 0.331358 0.616006
pneumonia 0.558435 0.863203 0.410861 0.424944 0.380598 0.640221
case 1.02514 1.20826 0.954097 0.951234 0.922763 1.09231
underway novemb addit develop covid imag \
trial 0.403056 0.397539 0.445364 0.681659 1.08631 0
vaccin 0.859493 0.849486 0.860979 0.980093 1.24608 0
stage 0.244095 0.248758 0.310649 0.633853 1.01181 0
late 0.297631 0.287544 0.350592 0.652365 1.00813 0
expect 0.289229 0.274898 0.337981 0.634082 1.00751 0
dose 0.632046 0.639533 0.63689 0.847946 1.11255 0
underway 7.45058e-09 0.233259 0.29835 0.607784 0.974593 0
novemb 0.233259 0 0.275329 0.598257 0.973511 0
addit 0.29835 0.275329 5.26836e-09 0.629283 0.973493 0
develop 0.607784 0.598257 0.629283 1.49012e-08 1.07383 0
covid 0.974593 0.973511 0.973493 1.07383 0 0
imag 0 0 0 0 0 0
use 0.622987 0.588229 0.611331 0.798822 1.07927 0
data 0.454566 0.454692 0.495457 0.675609 1.05223 0
model 0.323022 0.288787 0.32271 0.607936 0.964775 0
detect 0.21524 0.291492 0.332087 0.595602 0.960245 0
dataset 0 0 0 0 0 0
learn 0.285118 0.250348 0.293733 0.574115 0.948083 0
pneumonia 0.328271 0.299461 0.349617 0.610909 0.957274 0
case 0.930944 0.90975 0.935559 1.02839 1.2678 0
use data model detect dataset learn \
trial 0.746335 0.603135 0.551106 0.559059 0 0.525798
vaccin 1.00057 0.912835 0.824359 0.856794 0 0.835347
stage 0.642882 0.450508 0.403968 0.398969 0 0.374226
late 0.645935 0.526539 0.407541 0.409769 0 0.376633
expect 0.624019 0.492882 0.367668 0.366499 0 0.331358
dose 0.806973 0.734571 0.63727 0.628541 0 0.616006
underway 0.622987 0.454566 0.323022 0.21524 0 0.285118
novemb 0.588229 0.454692 0.288787 0.291492 0 0.250348
addit 0.611331 0.495457 0.32271 0.332087 0 0.293733
develop 0.798822 0.675609 0.607936 0.595602 0 0.574115
covid 1.07927 1.05223 0.964775 0.960245 0 0.948083
imag 0 0 0 0 0 0
use 0 0.591809 0.548085 0.582112 0 0.537211
data 0.591809 0 0.445673 0.441224 0 0.425729
model 0.548085 0.445673 2.63418e-09 0.236378 0 0.180477
detect 0.582112 0.441224 0.236378 0 0 0.183061
dataset 0 0 0 0 0 0
learn 0.537211 0.425729 0.180477 0.183061 0 0
pneumonia 0.578256 0.434679 0.241369 0.248207 0 0.199443
case 1.04105 0.983477 0.88739 0.901723 0 0.880812
pneumonia case
trial 0.558435 1.02514
vaccin 0.863203 1.20826
stage 0.410861 0.954097
late 0.424944 0.951234
expect 0.380598 0.922763
dose 0.640221 1.09231
underway 0.328271 0.930944
novemb 0.299461 0.90975
addit 0.349617 0.935559
develop 0.610909 1.02839
covid 0.957274 1.2678
imag 0 0
use 0.578256 1.04105
data 0.434679 0.983477
model 0.241369 0.88739
detect 0.248207 0.901723
dataset 0 0
learn 0.199443 0.880812
pneumonia 0 0.879138
case 0.879138 2.98023e-08
(-0.07008842952957238, 0.62502, -0.34371128200507034)
def mantel_test_verticle(model_a,model_b,n):
#all words from the cleaned documents
words_science = dict(model_a.show_topic(0, topn=word_count_science))
words_psy = dict(model_b.show_topic(0, topn=word_count_psy))
#top 5 words from the documents
top_words_science = dict(model_a.show_topic(0, topn=n))
top_words_psy = dict(model_b.show_topic(0,topn=n))
#Finding out the weights of top 5 law words in model_a level and updating their weights w.r.t model b dataset
c={}
for key in top_words_science:
try:
c[key] = words_psy[key]
except KeyError:
c[key] = 0
top_words_psy.update(c)
#Finding out the weights of top 5 law words in model b and updating their weights w.r.t model a dataset
b={}
for key in top_words_psy:
try:
b[key] = words_science[key]
except KeyError:
b[key] = 0
b.update(top_words_science)
top_words_science=b
#pairwise distance for top 10 words in model a
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
b=pd.DataFrame(words_from_science,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_science.keys()),
index = list(top_words_science.keys()))
l = list(top_words_science.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df1 = df.to_numpy()
#pairwise distance for top 10 words in model b
a=pd.DataFrame()
for i in range(number_of_topics):
words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
b=pd.DataFrame(words_from_psy,index=[i])
a=a.append(b)
a=a.transpose()
df = pd.DataFrame(columns=list(top_words_psy.keys()),
index = list(top_words_psy.keys()))
l = list(top_words_psy.keys())
print('Pairwise-distance')
for i in l:
for j in l:
try:
matrix=np.array([a.loc[i],a.loc[j]])
except KeyError:
matrix=np.zeros((2,3))
n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
df.loc[[i],[j]] = n[0,1]
print(df)
df2 = df.to_numpy()
dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])
dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])
return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')
mantel_test_verticle(model_science_articles,model_science_p,5)
Pairwise-distance
support 2020 granada covid programm imag \
support 0 0.816641 0.188647 1.34497 0.187055 1.80116
2020 0.816641 0 0.837019 1.50711 0.825008 1.99382
granada 0.188647 0.837019 0 1.38721 0.0724919 1.77803
covid 1.34497 1.50711 1.38721 0 1.3995 2.02757
programm 0.187055 0.825008 0.0724919 1.3995 0 1.77059
imag 1.80116 1.99382 1.77803 2.02757 1.77059 0
data 1.54057 1.91755 1.62324 1.71834 1.61751 2.52912
use 0.993312 1.24293 1.08391 1.47069 1.06767 1.9883
model 1.58459 1.58989 1.53724 2.01725 1.53708 2.45821
data use model
support 1.54057 0.993312 1.58459
2020 1.91755 1.24293 1.58989
granada 1.62324 1.08391 1.53724
covid 1.71834 1.47069 2.01725
programm 1.61751 1.06767 1.53708
imag 2.52912 1.9883 2.45821
data 4.71216e-08 1.95244 2.20772
use 1.95244 1.666e-08 1.81286
model 2.20772 1.81286 0
Pairwise-distance
support 2020 granada covid programm imag \
support 0 2.31274 1.14543 4.29945 0.893249 3.43628
2020 2.31274 0 1.24107 1.9984 1.7428 1.33987
granada 1.14543 1.24107 0 3.23626 0.524939 2.29085
covid 4.29945 1.9984 3.23626 0 3.72322 1.52372
programm 0.893249 1.7428 0.524939 3.72322 3.72529e-08 2.66513
imag 3.43628 1.33987 2.29085 1.52372 2.66513 0
data 1.7865 1.52372 0.913734 3.2917 0.893249 1.9984
use 2.85871 1.47712 1.7865 2.54797 1.98601 1.04988
model 2.85871 1.47712 1.7865 2.54797 1.98601 1.04988
data use model
support 1.7865 2.85871 2.85871
2020 1.52372 1.47712 1.47712
granada 0.913734 1.7865 1.7865
covid 3.2917 2.54797 2.54797
programm 0.893249 1.98601 1.98601
imag 1.9984 1.04988 1.04988
data 1.86265e-08 1.14543 1.14543
use 1.14543 0 0
model 1.14543 0 0
(0.07343425434503273, 0.3845, 0.3350390539943082)
mantel_test_verticle(model_science_articles,model_science_paragraph,5)
Pairwise-distance
covid imag use data model
covid 0 2.02757 1.47069 1.71834 2.01725
imag 2.02757 0 1.9883 2.52912 2.45821
use 1.47069 1.9883 1.666e-08 1.95244 1.81286
data 1.71834 2.52912 1.95244 4.71216e-08 2.20772
model 2.01725 2.45821 1.81286 2.20772 0
Pairwise-distance
covid imag use data model
covid 0 1.40421 1.40727 1.40273 1.40531
imag 1.40421 0 1.40628 1.40572 1.4035
use 1.40727 1.40628 0 1.40388 1.40595
data 1.40273 1.40572 1.40388 0 1.40268
model 1.40531 1.4035 1.40595 1.40268 2.10734e-08
(-0.32039903892431343, 0.7666666666666667, -0.8612915497586047)
mantel_test_verticle(model_science_p,model_science_paragraph,5)
Pairwise-distance
covid imag use data model support 2020 \
covid 0 1.52372 2.54797 3.2917 2.54797 4.29945 1.9984
imag 1.52372 0 1.04988 1.9984 1.04988 3.43628 1.33987
use 2.54797 1.04988 0 1.14543 0 2.85871 1.47712
data 3.2917 1.9984 1.14543 1.86265e-08 1.14543 1.7865 1.52372
model 2.54797 1.04988 0 1.14543 0 2.85871 1.47712
support 4.29945 3.43628 2.85871 1.7865 2.85871 0 2.31274
2020 1.9984 1.33987 1.47712 1.52372 1.47712 2.31274 0
granada 3.23626 2.29085 1.7865 0.913734 1.7865 1.14543 1.24107
programm 3.72322 2.66513 1.98601 0.893249 1.98601 0.893249 1.7428
granada programm
covid 3.23626 3.72322
imag 2.29085 2.66513
use 1.7865 1.98601
data 0.913734 0.893249
model 1.7865 1.98601
support 1.14543 0.893249
2020 1.24107 1.7428
granada 0 0.524939
programm 0.524939 3.72529e-08
Pairwise-distance
covid imag use data model support \
covid 0 1.40421 1.40727 1.40273 1.40531 1.06278
imag 1.40421 0 1.40628 1.40572 1.4035 1.06418
use 1.40727 1.40628 0 1.40388 1.40595 1.06854
data 1.40273 1.40572 1.40388 0 1.40268 1.05647
model 1.40531 1.4035 1.40595 1.40268 2.10734e-08 1.06304
support 1.06278 1.06418 1.06854 1.05647 1.06304 7.45058e-09
2020 1.37247 1.36764 1.3705 1.36589 1.37095 1.00675
granada 0.994919 0.993477 0.995627 0.98926 0.993864 0.375134
programm 0.995038 0.993206 0.995886 0.990081 0.993839 0.356717
2020 granada programm
covid 1.37247 0.994919 0.995038
imag 1.36764 0.993477 0.993206
use 1.3705 0.995627 0.995886
data 1.36589 0.98926 0.990081
model 1.37095 0.993864 0.993839
support 1.00675 0.375134 0.356717
2020 0 0.942932 0.943122
granada 0.942932 2.32831e-10 0.0246327
programm 0.943122 0.0246327 4.65661e-10
(0.10610382106187127, 0.3988, 0.4236923509288503)
mantel_test_verticle(model_psy_articles,model_psy_p,5)
Pairwise-distance
misinfodem covid articl commun mechan 2020 \
misinfodem 8.33e-09 1.34189 0.738596 0.47164 0.410331 1.55979
covid 1.34189 0 1.23555 1.3068 1.27012 1.64398
articl 0.738596 1.23555 1.17804e-08 0.689709 0.459477 1.31627
commun 0.47164 1.3068 0.689709 1.17804e-08 0.371932 1.4797
mechan 0.410331 1.27012 0.459477 0.371932 2.9451e-09 1.4595
2020 1.55979 1.64398 1.31627 1.4797 1.4595 0
psycholog 1.33778 1.5689 1.104 1.07665 1.13502 1.6757
social 1.03298 1.63894 1.17862 1.03988 1.00005 1.6997
epidem 1.16331 1.65599 1.11923 1.11811 0.981829 1.68517
psycholog social epidem
misinfodem 1.33778 1.03298 1.16331
covid 1.5689 1.63894 1.65599
articl 1.104 1.17862 1.11923
commun 1.07665 1.03988 1.11811
mechan 1.13502 1.00005 0.981829
2020 1.6757 1.6997 1.68517
psycholog 1.666e-08 1.40467 1.36759
social 1.40467 0 1.05316
epidem 1.36759 1.05316 3.332e-08
Pairwise-distance
misinfodem covid articl commun mechan 2020 \
misinfodem 0 1.33926 2.07845 2.96288 2.96288 3.31627
covid 1.33926 0 1.10881 1.65813 1.65813 2.07845
articl 2.07845 1.10881 0 1.20521 1.20521 1.33926
commun 2.96288 1.65813 1.20521 0 0 0.554403
mechan 2.96288 1.65813 1.20521 0 0 0.554403
2020 3.31627 2.07845 1.33926 0.554403 0.554403 0
psycholog 2.55053 1.66321 0.554403 1.31385 1.31385 1.20521
social 4.01777 2.67852 2.41043 1.20521 1.20521 1.31385
epidem 0 0 0 0 0 0
psycholog social epidem
misinfodem 2.55053 4.01777 0
covid 1.66321 2.67852 0
articl 0.554403 2.41043 0
commun 1.31385 1.20521 0
mechan 1.31385 1.20521 0
2020 1.20521 1.31385 0
psycholog 0 2.45969 0
social 2.45969 0 0
epidem 0 0 0
(-0.06374745393273847, 0.5867, -0.2263477316643351)
mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)
Pairwise-distance
2020 covid pandem health polici psycholog social \
2020 0 1.64398 1.21093 1.63587 1.38208 1.6757 1.6997
covid 1.64398 0 1.24569 1.92675 1.26996 1.5689 1.63894
pandem 1.21093 1.24569 0 1.41086 0.788078 1.17936 1.32541
health 1.63587 1.92675 1.41086 0 1.46491 1.41224 1.67666
polici 1.38208 1.26996 0.788078 1.46491 8.33e-09 1.0708 1.13705
psycholog 1.6757 1.5689 1.17936 1.41224 1.0708 1.666e-08 1.40467
social 1.6997 1.63894 1.32541 1.67666 1.13705 1.40467 0
epidem 1.68517 1.65599 1.34219 1.68828 1.03278 1.36759 1.05316
epidem
2020 1.68517
covid 1.65599
pandem 1.34219
health 1.68828
polici 1.03278
psycholog 1.36759
social 1.05316
epidem 3.332e-08
Pairwise-distance
2020 covid pandem health polici psycholog \
2020 2.98023e-08 1.40859 1.40457 1.39359 1.1861 1.40269
covid 1.40859 0 1.40314 1.39289 1.18071 1.39959
pandem 1.40457 1.40314 2.98023e-08 1.38902 1.18354 1.39676
health 1.39359 1.39289 1.38902 0 1.16427 1.38385
polici 1.1861 1.18071 1.18354 1.16427 1.05367e-08 1.17642
psycholog 1.40269 1.39959 1.39676 1.38385 1.17642 0
social 1.38116 1.37955 1.37681 1.36128 1.1579 1.37572
epidem 1.38005 1.37922 1.3742 1.36229 1.14678 1.36667
social epidem
2020 1.38116 1.38005
covid 1.37955 1.37922
pandem 1.37681 1.3742
health 1.36128 1.36229
polici 1.1579 1.14678
psycholog 1.37572 1.36667
social 0 1.34363
epidem 1.34363 0
(0.5650443081417844, 0.0453, 1.6640248141204788)
mantel_test_verticle(model_psy_p,model_psy_paragraph,5)
Pairwise-distance
2020 covid pandem health polici misinfodem \
2020 0 2.07845 1.31385 0.554403 0 3.31627
covid 2.07845 0 2.67852 1.65813 0 1.33926
pandem 1.31385 2.67852 0 1.20521 0 4.01777
health 0.554403 1.65813 1.20521 0 0 2.96288
polici 0 0 0 0 0 0
misinfodem 3.31627 1.33926 4.01777 2.96288 0 0
articl 1.33926 1.10881 2.41043 1.20521 0 2.07845
commun 0.554403 1.65813 1.20521 0 0 2.96288
mechan 0.554403 1.65813 1.20521 1.31709e-08 0 2.96288
articl commun mechan
2020 1.33926 0.554403 0.554403
covid 1.10881 1.65813 1.65813
pandem 2.41043 1.20521 1.20521
health 1.20521 0 1.31709e-08
polici 0 0 0
misinfodem 2.07845 2.96288 2.96288
articl 0 1.20521 1.20521
commun 1.20521 0 0
mechan 1.20521 0 0
Pairwise-distance
2020 covid pandem health polici \
2020 2.98023e-08 1.40859 1.40457 1.39359 1.1861
covid 1.40859 0 1.40314 1.39289 1.18071
pandem 1.40457 1.40314 2.98023e-08 1.38902 1.18354
health 1.39359 1.39289 1.38902 0 1.16427
polici 1.1861 1.18071 1.18354 1.16427 1.05367e-08
misinfodem 1.00964 0.998188 0.99604 0.990675 0.666441
articl 1.25618 1.24364 1.23712 1.23471 0.99842
commun 1.3593 1.35413 1.35723 1.33539 1.1385
mechan 0.997926 0.993957 0.991672 0.975204 0.640464
misinfodem articl commun mechan
2020 1.00964 1.25618 1.3593 0.997926
covid 0.998188 1.24364 1.35413 0.993957
pandem 0.99604 1.23712 1.35723 0.991672
health 0.990675 1.23471 1.33539 0.975204
polici 0.666441 0.99842 1.1385 0.640464
misinfodem 0 0.762354 0.952795 0.124054
articl 0.762354 0 1.19818 0.754808
commun 0.952795 1.19818 2.10734e-08 0.927779
mechan 0.124054 0.754808 0.927779 0
(-0.11802488631885512, 0.6568, -0.4046262850059532)
mantel_test_verticle(model_science_articles,model_science_p,10)
Pairwise-distance
support 2020 granada covid research programm \
support 0 0.816641 0.188647 1.34497 0.327252 0.187055
2020 0.816641 0 0.837019 1.50711 0.857407 0.825008
granada 0.188647 0.837019 0 1.38721 0.268729 0.0724919
covid 1.34497 1.50711 1.38721 0 1.32888 1.3995
research 0.327252 0.857407 0.268729 1.32888 0 0.247326
programm 0.187055 0.825008 0.0724919 1.3995 0.247326 0
tabik 0.207205 0.849983 0.0543689 1.38042 0.295629 0.126861
garcía 0.184286 0.830191 0.0420849 1.39198 0.254454 0.0354088
decsai 0.184807 0.827432 0.0543689 1.39609 0.250891 0.018123
project 0.280371 0.843909 0.24052 1.42673 0.293543 0.201352
imag 1.80116 1.99382 1.77803 2.02757 1.7596 1.77059
data 1.54057 1.91755 1.62324 1.71834 1.56879 1.61751
use 0.993312 1.24293 1.08391 1.47069 1.09865 1.06767
model 1.58459 1.58989 1.53724 2.01725 1.56162 1.53708
dataset 1.27474 1.59928 1.26197 1.99076 1.26775 1.27385
class 1.05441 1.27301 1.02538 1.43477 1.1235 1.0666
learn 1.24747 1.45402 1.22658 1.67678 1.24625 1.21551
perform 0.790359 1.0249 0.807618 1.47021 0.819504 0.779769
train 0.804701 1.02548 0.756157 1.32819 0.727077 0.735173
tabik garcía decsai project imag data \
support 0.207205 0.184286 0.184807 0.280371 1.80116 1.54057
2020 0.849983 0.830191 0.827432 0.843909 1.99382 1.91755
granada 0.0543689 0.0420849 0.0543689 0.24052 1.77803 1.62324
covid 1.38042 1.39198 1.39609 1.42673 2.02757 1.71834
research 0.295629 0.254454 0.250891 0.293543 1.7596 1.56879
programm 0.126861 0.0354088 0.018123 0.201352 1.77059 1.61751
tabik 0 0.0951653 0.108738 0.278797 1.78553 1.62964
garcía 0.0951653 0 0.0199448 0.206467 1.77333 1.62034
decsai 0.108738 0.0199448 0 0.209485 1.77218 1.61864
project 0.278797 0.206467 0.209485 5.10106e-09 1.74898 1.64049
imag 1.78553 1.77333 1.77218 1.74898 0 2.52912
data 1.62964 1.62034 1.61864 1.64049 2.52912 4.71216e-08
use 1.09907 1.07214 1.0713 1.0301 1.9883 1.95244
model 1.5396 1.53774 1.5368 1.57066 2.45821 2.20772
dataset 1.25573 1.26927 1.2705 1.26058 1.97696 1.90756
class 0.996813 1.03919 1.05598 1.02701 2.04976 2.03687
learn 1.23761 1.22072 1.21788 1.23169 2.19157 2.13977
perform 0.832046 0.793742 0.786197 0.806445 1.88734 1.76081
train 0.775977 0.746538 0.739809 0.768763 1.58582 1.79814
use model dataset class learn perform train
support 0.993312 1.58459 1.27474 1.05441 1.24747 0.790359 0.804701
2020 1.24293 1.58989 1.59928 1.27301 1.45402 1.0249 1.02548
granada 1.08391 1.53724 1.26197 1.02538 1.22658 0.807618 0.756157
covid 1.47069 2.01725 1.99076 1.43477 1.67678 1.47021 1.32819
research 1.09865 1.56162 1.26775 1.1235 1.24625 0.819504 0.727077
programm 1.06767 1.53708 1.27385 1.0666 1.21551 0.779769 0.735173
tabik 1.09907 1.5396 1.25573 0.996813 1.23761 0.832046 0.775977
garcía 1.07214 1.53774 1.26927 1.03919 1.22072 0.793742 0.746538
decsai 1.0713 1.5368 1.2705 1.05598 1.21788 0.786197 0.739809
project 1.0301 1.57066 1.26058 1.02701 1.23169 0.806445 0.768763
imag 1.9883 2.45821 1.97696 2.04976 2.19157 1.88734 1.58582
data 1.95244 2.20772 1.90756 2.03687 2.13977 1.76081 1.79814
use 1.666e-08 1.81286 1.55914 1.47649 1.47647 1.05944 1.37724
model 1.81286 0 2.00352 1.82546 1.924 1.26853 1.56263
dataset 1.55914 2.00352 0 1.59981 1.83856 1.50625 1.52998
class 1.47649 1.82546 1.59981 0 1.56226 1.41108 1.26185
learn 1.47647 1.924 1.83856 1.56226 0 1.34012 1.29341
perform 1.05944 1.26853 1.50625 1.41108 1.34012 0 0.904781
train 1.37724 1.56263 1.52998 1.26185 1.29341 0.904781 0
Pairwise-distance
support 2020 granada covid research programm \
support 0 2.31274 1.14543 4.29945 0.893249 0.893249
2020 2.31274 0 1.24107 1.9984 1.7428 1.7428
granada 1.14543 1.24107 0 3.23626 0.524939 0.524939
covid 4.29945 1.9984 3.23626 0 3.72322 3.72322
research 0.893249 1.7428 0.524939 3.72322 3.72529e-08 3.72529e-08
programm 0.893249 1.7428 0.524939 3.72322 3.72529e-08 3.72529e-08
tabik 1.55841 1.04988 0.446625 2.97988 0.770914 0.770914
garcía 2.29085 0.596458 1.14543 2.25512 1.54183 1.54183
decsai 1.33987 1.57482 0.596458 3.4856 0.446625 0.446625
project 1.33987 1.57482 0.596458 3.4856 0.446625 0.446625
imag 3.43628 1.33987 2.29085 1.52372 2.66513 2.66513
data 1.7865 1.52372 0.913734 3.2917 0.893249 0.893249
use 2.85871 1.47712 1.7865 2.54797 1.98601 1.98601
model 2.85871 1.47712 1.7865 2.54797 1.98601 1.98601
dataset 2.85871 1.47712 1.7865 2.54797 1.98601 1.98601
class 0 0 0 0 0 0
learn 2.85871 1.47712 1.7865 2.54797 1.98601 1.98601
perform 0 0 0 0 0 0
train 0 0 0 0 0 0
tabik garcía decsai project imag data \
support 1.55841 2.29085 1.33987 1.33987 3.43628 1.7865
2020 1.04988 0.596458 1.57482 1.57482 1.33987 1.52372
granada 0.446625 1.14543 0.596458 0.596458 2.29085 0.913734
covid 2.97988 2.25512 3.4856 3.4856 1.52372 3.2917
research 0.770914 1.54183 0.446625 0.446625 2.66513 0.893249
programm 0.770914 1.54183 0.446625 0.446625 2.66513 0.893249
tabik 0 0.770914 0.524939 0.524939 1.90083 0.596458
garcía 0.770914 0 1.24107 1.24107 1.14543 1.04988
decsai 0.524939 1.24107 0 0 2.31274 0.446625
project 0.524939 1.24107 0 0 2.31274 0.446625
imag 1.90083 1.14543 2.31274 2.31274 0 1.9984
data 0.596458 1.04988 0.446625 0.446625 1.9984 1.86265e-08
use 1.33987 0.913734 1.55841 1.55841 1.04988 1.14543
model 1.33987 0.913734 1.55841 1.55841 1.04988 1.14543
dataset 1.33987 0.913734 1.55841 1.55841 1.04988 1.14543
class 0 0 0 0 0 0
learn 1.33987 0.913734 1.55841 1.55841 1.04988 1.14543
perform 0 0 0 0 0 0
train 0 0 0 0 0 0
use model dataset class learn perform train
support 2.85871 2.85871 2.85871 0 2.85871 0 0
2020 1.47712 1.47712 1.47712 0 1.47712 0 0
granada 1.7865 1.7865 1.7865 0 1.7865 0 0
covid 2.54797 2.54797 2.54797 0 2.54797 0 0
research 1.98601 1.98601 1.98601 0 1.98601 0 0
programm 1.98601 1.98601 1.98601 0 1.98601 0 0
tabik 1.33987 1.33987 1.33987 0 1.33987 0 0
garcía 0.913734 0.913734 0.913734 0 0.913734 0 0
decsai 1.55841 1.55841 1.55841 0 1.55841 0 0
project 1.55841 1.55841 1.55841 0 1.55841 0 0
imag 1.04988 1.04988 1.04988 0 1.04988 0 0
data 1.14543 1.14543 1.14543 0 1.14543 0 0
use 0 0 6.58545e-09 0 0 0 0
model 0 0 6.58545e-09 0 0 0 0
dataset 6.58545e-09 6.58545e-09 0 0 0 0 0
class 0 0 0 0 0 0 0
learn 0 0 0 0 0 0 0
perform 0 0 0 0 0 0 0
train 0 0 0 0 0 0 0
(0.1922992491228527, 0.1557, 1.0440487307871915)
mantel_test_verticle(model_science_articles,model_science_paragraph,10)
Pairwise-distance
covid imag use data model detect \
covid 0 2.02757 1.47069 1.71834 2.01725 1.49496
imag 2.02757 0 1.9883 2.52912 2.45821 1.80145
use 1.47069 1.9883 1.666e-08 1.95244 1.81286 1.35039
data 1.71834 2.52912 1.95244 4.71216e-08 2.20772 1.72029
model 2.01725 2.45821 1.81286 2.20772 0 1.53885
detect 1.49496 1.80145 1.35039 1.72029 1.53885 1.666e-08
dataset 1.99076 1.97696 1.55914 1.90756 2.00352 1.55985
learn 1.67678 2.19157 1.47647 2.13977 1.924 1.36813
pneumonia 1.51124 1.65107 1.29053 1.92202 1.62318 1.14483
case 1.8398 2.10333 1.4916 1.79869 1.76951 1.42734
class 1.43477 2.04976 1.47649 2.03687 1.82546 1.27213
perform 1.47021 1.88734 1.05944 1.76081 1.26853 0.97356
train 1.32819 1.58582 1.37724 1.79814 1.56263 0.91949
dataset learn pneumonia case class perform train
covid 1.99076 1.67678 1.51124 1.8398 1.43477 1.47021 1.32819
imag 1.97696 2.19157 1.65107 2.10333 2.04976 1.88734 1.58582
use 1.55914 1.47647 1.29053 1.4916 1.47649 1.05944 1.37724
data 1.90756 2.13977 1.92202 1.79869 2.03687 1.76081 1.79814
model 2.00352 1.924 1.62318 1.76951 1.82546 1.26853 1.56263
detect 1.55985 1.36813 1.14483 1.42734 1.27213 0.97356 0.91949
dataset 0 1.83856 1.62487 1.56521 1.59981 1.50625 1.52998
learn 1.83856 0 1.44422 1.70748 1.56226 1.34012 1.29341
pneumonia 1.62487 1.44422 2.35608e-08 1.45796 1.27669 0.859783 0.858428
case 1.56521 1.70748 1.45796 0 1.60019 1.44212 1.35873
class 1.59981 1.56226 1.27669 1.60019 0 1.41108 1.26185
perform 1.50625 1.34012 0.859783 1.44212 1.41108 0 0.904781
train 1.52998 1.29341 0.858428 1.35873 1.26185 0.904781 0
Pairwise-distance
covid imag use data model detect \
covid 0 1.40421 1.40727 1.40273 1.40531 1.37374
imag 1.40421 0 1.40628 1.40572 1.4035 1.38102
use 1.40727 1.40628 0 1.40388 1.40595 1.37802
data 1.40273 1.40572 1.40388 0 1.40268 1.37488
model 1.40531 1.4035 1.40595 1.40268 2.10734e-08 1.37696
detect 1.37374 1.38102 1.37802 1.37488 1.37696 0
dataset 1.40378 1.40559 1.40461 1.40323 1.40448 1.37935
learn 1.38162 1.37922 1.38403 1.37655 1.37925 1.35772
pneumonia 1.35568 1.35395 1.35473 1.35165 1.35503 1.32098
case 1.40363 1.40305 1.40457 1.4018 1.40358 1.37442
class 1.40066 1.40139 1.40288 1.39873 1.40067 1.37328
perform 1.39968 1.39832 1.39973 1.39625 1.39996 1.36945
train 1.40488 1.40369 1.40465 1.40045 1.40176 1.37614
dataset learn pneumonia case class \
covid 1.40378 1.38162 1.35568 1.40363 1.40066
imag 1.40559 1.37922 1.35395 1.40305 1.40139
use 1.40461 1.38403 1.35473 1.40457 1.40288
data 1.40323 1.37655 1.35165 1.4018 1.39873
model 1.40448 1.37925 1.35503 1.40358 1.40067
detect 1.37935 1.35772 1.32098 1.37442 1.37328
dataset 1.49012e-08 1.37892 1.35471 1.40317 1.40065
learn 1.37892 0 1.33013 1.37961 1.37412
pneumonia 1.35471 1.33013 3.332e-08 1.35182 1.34286
case 1.40317 1.37961 1.35182 2.10734e-08 1.40016
class 1.40065 1.37412 1.34286 1.40016 2.58096e-08
perform 1.39746 1.37242 1.34808 1.39701 1.3957
train 1.40271 1.37919 1.35335 1.40102 1.39916
perform train
covid 1.39968 1.40488
imag 1.39832 1.40369
use 1.39973 1.40465
data 1.39625 1.40045
model 1.39996 1.40176
detect 1.36945 1.37614
dataset 1.39746 1.40271
learn 1.37242 1.37919
pneumonia 1.34808 1.35335
case 1.39701 1.40102
class 1.3957 1.39916
perform 3.65002e-08 1.39794
train 1.39794 1.49012e-08
(0.3987595628510933, 0.0671, 1.4780991767816107)
mantel_test_verticle(model_science_p,model_science_paragraph,10)
Pairwise-distance
covid imag use data model \
covid 0 1.52372 2.54797 3.2917 2.54797
imag 1.52372 0 1.04988 1.9984 1.04988
use 2.54797 1.04988 0 1.14543 0
data 3.2917 1.9984 1.14543 1.86265e-08 1.14543
model 2.54797 1.04988 0 1.14543 0
detect 2.54797 1.04988 0 1.14543 0
dataset 2.54797 1.04988 6.58545e-09 1.14543 6.58545e-09
learn 2.54797 1.04988 0 1.14543 0
pneumonia 0 0 0 0 0
case 2.03258 0.524939 0.524939 1.54183 0.524939
support 4.29945 3.43628 2.85871 1.7865 2.85871
2020 1.9984 1.33987 1.47712 1.52372 1.47712
granada 3.23626 2.29085 1.7865 0.913734 1.7865
research 3.72322 2.66513 1.98601 0.893249 1.98601
programm 3.72322 2.66513 1.98601 0.893249 1.98601
tabik 2.97988 1.90083 1.33987 0.596458 1.33987
garcía 2.25512 1.14543 0.913734 1.04988 0.913734
decsai 3.4856 2.31274 1.55841 0.446625 1.55841
project 3.4856 2.31274 1.55841 0.446625 1.55841
detect dataset learn pneumonia case support \
covid 2.54797 2.54797 2.54797 0 2.03258 4.29945
imag 1.04988 1.04988 1.04988 0 0.524939 3.43628
use 0 6.58545e-09 0 0 0.524939 2.85871
data 1.14543 1.14543 1.14543 0 1.54183 1.7865
model 0 6.58545e-09 0 0 0.524939 2.85871
detect 0 6.58545e-09 0 0 0.524939 2.85871
dataset 6.58545e-09 0 0 0 0.524939 2.85871
learn 0 0 0 0 0.524939 2.85871
pneumonia 0 0 0 0 0 0
case 0.524939 0.524939 0.524939 0 0 3.11682
support 2.85871 2.85871 2.85871 0 3.11682 0
2020 1.47712 1.47712 1.47712 0 1.30882 2.31274
granada 1.7865 1.7865 1.7865 0 1.98601 1.14543
research 1.98601 1.98601 1.98601 0 2.29085 0.893249
programm 1.98601 1.98601 1.98601 0 2.29085 0.893249
tabik 1.33987 1.33987 1.33987 0 1.55841 1.55841
garcía 0.913734 0.913734 0.913734 0 0.893249 2.29085
decsai 1.55841 1.55841 1.55841 0 1.90083 1.33987
project 1.55841 1.55841 1.55841 0 1.90083 1.33987
2020 granada research programm tabik garcía \
covid 1.9984 3.23626 3.72322 3.72322 2.97988 2.25512
imag 1.33987 2.29085 2.66513 2.66513 1.90083 1.14543
use 1.47712 1.7865 1.98601 1.98601 1.33987 0.913734
data 1.52372 0.913734 0.893249 0.893249 0.596458 1.04988
model 1.47712 1.7865 1.98601 1.98601 1.33987 0.913734
detect 1.47712 1.7865 1.98601 1.98601 1.33987 0.913734
dataset 1.47712 1.7865 1.98601 1.98601 1.33987 0.913734
learn 1.47712 1.7865 1.98601 1.98601 1.33987 0.913734
pneumonia 0 0 0 0 0 0
case 1.30882 1.98601 2.29085 2.29085 1.55841 0.893249
support 2.31274 1.14543 0.893249 0.893249 1.55841 2.29085
2020 0 1.24107 1.7428 1.7428 1.04988 0.596458
granada 1.24107 0 0.524939 0.524939 0.446625 1.14543
research 1.7428 0.524939 3.72529e-08 3.72529e-08 0.770914 1.54183
programm 1.7428 0.524939 3.72529e-08 3.72529e-08 0.770914 1.54183
tabik 1.04988 0.446625 0.770914 0.770914 0 0.770914
garcía 0.596458 1.14543 1.54183 1.54183 0.770914 0
decsai 1.57482 0.596458 0.446625 0.446625 0.524939 1.24107
project 1.57482 0.596458 0.446625 0.446625 0.524939 1.24107
decsai project
covid 3.4856 3.4856
imag 2.31274 2.31274
use 1.55841 1.55841
data 0.446625 0.446625
model 1.55841 1.55841
detect 1.55841 1.55841
dataset 1.55841 1.55841
learn 1.55841 1.55841
pneumonia 0 0
case 1.90083 1.90083
support 1.33987 1.33987
2020 1.57482 1.57482
granada 0.596458 0.596458
research 0.446625 0.446625
programm 0.446625 0.446625
tabik 0.524939 0.524939
garcía 1.24107 1.24107
decsai 0 0
project 0 0
Pairwise-distance
covid imag use data model detect \
covid 0 1.40421 1.40727 1.40273 1.40531 1.37374
imag 1.40421 0 1.40628 1.40572 1.4035 1.38102
use 1.40727 1.40628 0 1.40388 1.40595 1.37802
data 1.40273 1.40572 1.40388 0 1.40268 1.37488
model 1.40531 1.4035 1.40595 1.40268 2.10734e-08 1.37696
detect 1.37374 1.38102 1.37802 1.37488 1.37696 0
dataset 1.40378 1.40559 1.40461 1.40323 1.40448 1.37935
learn 1.38162 1.37922 1.38403 1.37655 1.37925 1.35772
pneumonia 1.35568 1.35395 1.35473 1.35165 1.35503 1.32098
case 1.40363 1.40305 1.40457 1.4018 1.40358 1.37442
support 1.06278 1.06418 1.06854 1.05647 1.06304 1.03184
2020 1.37247 1.36764 1.3705 1.36589 1.37095 1.34249
granada 0.994919 0.993477 0.995627 0.98926 0.993864 0.954406
research 1.10123 1.09918 1.10233 1.09244 1.10677 1.05293
programm 0.995038 0.993206 0.995886 0.990081 0.993839 0.954995
tabik 0.989382 0.990932 0.997691 0.990737 0.996166 0.960675
garcía 0.994712 0.992939 0.9954 0.989892 0.99337 0.954772
decsai 0.994692 0.992952 0.995401 0.989883 0.993313 0.95459
project 0.998161 0.993764 0.994315 0.991023 0.994203 0.957608
dataset learn pneumonia case support \
covid 1.40378 1.38162 1.35568 1.40363 1.06278
imag 1.40559 1.37922 1.35395 1.40305 1.06418
use 1.40461 1.38403 1.35473 1.40457 1.06854
data 1.40323 1.37655 1.35165 1.4018 1.05647
model 1.40448 1.37925 1.35503 1.40358 1.06304
detect 1.37935 1.35772 1.32098 1.37442 1.03184
dataset 1.49012e-08 1.37892 1.35471 1.40317 1.06129
learn 1.37892 0 1.33013 1.37961 1.03572
pneumonia 1.35471 1.33013 3.332e-08 1.35182 0.998452
case 1.40317 1.37961 1.35182 2.10734e-08 1.06188
support 1.06129 1.03572 0.998452 1.06188 7.45058e-09
2020 1.36788 1.34561 1.32336 1.36907 1.00675
granada 0.992726 0.957442 0.921466 0.991552 0.375134
research 1.0982 1.0727 1.04357 1.10407 0.371799
programm 0.992533 0.958047 0.921611 0.991307 0.356717
tabik 0.984802 0.959439 0.924422 0.992796 0.379168
garcía 0.992227 0.957386 0.921346 0.991095 0.378892
decsai 0.992279 0.957428 0.921096 0.991277 0.378799
project 0.993346 0.962198 0.921511 0.989514 0.354371
2020 granada research programm tabik \
covid 1.37247 0.994919 1.10123 0.995038 0.989382
imag 1.36764 0.993477 1.09918 0.993206 0.990932
use 1.3705 0.995627 1.10233 0.995886 0.997691
data 1.36589 0.98926 1.09244 0.990081 0.990737
model 1.37095 0.993864 1.10677 0.993839 0.996166
detect 1.34249 0.954406 1.05293 0.954995 0.960675
dataset 1.36788 0.992726 1.0982 0.992533 0.984802
learn 1.34561 0.957442 1.0727 0.958047 0.959439
pneumonia 1.32336 0.921466 1.04357 0.921611 0.924422
case 1.36907 0.991552 1.10407 0.991307 0.992796
support 1.00675 0.375134 0.371799 0.356717 0.379168
2020 0 0.942932 1.05537 0.943122 0.945404
granada 0.942932 2.32831e-10 0.46932 0.0246327 0.0414022
research 1.05537 0.46932 0 0.453623 0.469499
programm 0.943122 0.0246327 0.453623 4.65661e-10 0.0437944
tabik 0.945404 0.0414022 0.469499 0.0437944 0
garcía 0.942555 0.0164708 0.471534 0.0241717 0.038013
decsai 0.942713 0.0144416 0.472001 0.0235131 0.0393642
project 0.939722 0.0611338 0.456785 0.0586793 0.0782135
garcía decsai project
covid 0.994712 0.994692 0.998161
imag 0.992939 0.992952 0.993764
use 0.9954 0.995401 0.994315
data 0.989892 0.989883 0.991023
model 0.99337 0.993313 0.994203
detect 0.954772 0.95459 0.957608
dataset 0.992227 0.992279 0.993346
learn 0.957386 0.957428 0.962198
pneumonia 0.921346 0.921096 0.921511
case 0.991095 0.991277 0.989514
support 0.378892 0.378799 0.354371
2020 0.942555 0.942713 0.939722
granada 0.0164708 0.0144416 0.0611338
research 0.471534 0.472001 0.456785
programm 0.0241717 0.0235131 0.0586793
tabik 0.038013 0.0393642 0.0782135
garcía 0 0.00732907 0.0653737
decsai 0.00732907 5.04093e-11 0.0646922
project 0.0653737 0.0646922 0
(0.07489061944996013, 0.3574, 0.42684320312414537)
mantel_test_verticle(model_psy_articles,model_psy_p,10)
Pairwise-distance
misinfodem covid articl commun mechan health \
misinfodem 8.33e-09 1.34189 0.738596 0.47164 0.410331 1.46436
covid 1.34189 0 1.23555 1.3068 1.27012 1.92675
articl 0.738596 1.23555 1.17804e-08 0.689709 0.459477 1.41907
commun 0.47164 1.3068 0.689709 1.17804e-08 0.371932 1.39573
mechan 0.410331 1.27012 0.459477 0.371932 2.9451e-09 1.39619
health 1.46436 1.92675 1.41907 1.39573 1.39619 0
strategi 0.367223 1.29332 0.52747 0.336412 0.177941 1.36005
psycholog 1.33778 1.5689 1.104 1.07665 1.13502 1.41224
misinform 0.219782 1.29879 0.573296 0.366512 0.193868 1.41432
inform 0.400083 1.28014 0.618093 0.336857 0.346339 1.50084
2020 1.55979 1.64398 1.31627 1.4797 1.4595 1.63587
social 1.03298 1.63894 1.17862 1.03988 1.00005 1.67666
epidem 1.16331 1.65599 1.11923 1.11811 0.981829 1.68828
pandem 0.935609 1.24569 0.751603 0.941839 0.805615 1.41086
time 0.835836 1.41466 0.708098 0.728133 0.630183 1.495
group 1.82751 2.09422 1.67686 1.83891 1.7099 2.24603
peopl 1.21721 1.65426 1.10551 1.12996 1.11525 1.88704
strategi psycholog misinform inform 2020 social \
misinfodem 0.367223 1.33778 0.219782 0.400083 1.55979 1.03298
covid 1.29332 1.5689 1.29879 1.28014 1.64398 1.63894
articl 0.52747 1.104 0.573296 0.618093 1.31627 1.17862
commun 0.336412 1.07665 0.366512 0.336857 1.4797 1.03988
mechan 0.177941 1.13502 0.193868 0.346339 1.4595 1.00005
health 1.36005 1.41224 1.41432 1.50084 1.63587 1.67666
strategi 0 1.14429 0.195458 0.360005 1.44692 1.02535
psycholog 1.14429 1.666e-08 1.21392 1.19198 1.6757 1.40467
misinform 0.195458 1.21392 0 0.309496 1.48789 0.985289
inform 0.360005 1.19198 0.309496 8.33e-09 1.49885 0.943419
2020 1.44692 1.6757 1.48789 1.49885 0 1.6997
social 1.02535 1.40467 0.985289 0.943419 1.6997 0
epidem 1.0459 1.36759 1.03317 1.10397 1.68517 1.05316
pandem 0.876623 1.17936 0.850458 0.918664 1.21093 1.32541
time 0.619814 1.14433 0.695799 0.758514 1.37622 0.916963
group 1.74272 2.04504 1.76019 1.75681 2.23965 1.78649
peopl 1.1706 1.53597 1.14825 1.0274 1.86356 1.206
epidem pandem time group peopl
misinfodem 1.16331 0.935609 0.835836 1.82751 1.21721
covid 1.65599 1.24569 1.41466 2.09422 1.65426
articl 1.11923 0.751603 0.708098 1.67686 1.10551
commun 1.11811 0.941839 0.728133 1.83891 1.12996
mechan 0.981829 0.805615 0.630183 1.7099 1.11525
health 1.68828 1.41086 1.495 2.24603 1.88704
strategi 1.0459 0.876623 0.619814 1.74272 1.1706
psycholog 1.36759 1.17936 1.14433 2.04504 1.53597
misinform 1.03317 0.850458 0.695799 1.76019 1.14825
inform 1.10397 0.918664 0.758514 1.75681 1.0274
2020 1.68517 1.21093 1.37622 2.23965 1.86356
social 1.05316 1.32541 0.916963 1.78649 1.206
epidem 3.332e-08 1.34219 0.984028 2.02774 1.54428
pandem 1.34219 0 1.06184 1.9243 1.24942
time 0.984028 1.06184 1.666e-08 1.7937 1.20018
group 2.02774 1.9243 1.7937 3.332e-08 1.76392
peopl 1.54428 1.24942 1.20018 1.76392 0
Pairwise-distance
misinfodem covid articl commun mechan health \
misinfodem 0 1.33926 2.07845 2.96288 2.96288 2.96288
covid 1.33926 0 1.10881 1.65813 1.65813 1.65813
articl 2.07845 1.10881 0 1.20521 1.20521 1.20521
commun 2.96288 1.65813 1.20521 0 0 0
mechan 2.96288 1.65813 1.20521 0 0 1.31709e-08
health 2.96288 1.65813 1.20521 0 1.31709e-08 0
strategi 2.55053 1.66321 0.554403 1.31385 1.31385 1.31385
psycholog 2.55053 1.66321 0.554403 1.31385 1.31385 1.31385
misinform 3.57135 2.2321 2.01252 0.819898 0.819898 0.819898
inform 2.89364 1.70035 0.892838 0.538987 0.538987 0.538987
2020 3.31627 2.07845 1.33926 0.554403 0.554403 0.554403
social 4.01777 2.67852 2.41043 1.20521 1.20521 1.20521
epidem 0 0 0 0 0 0
pandem 4.01777 2.67852 2.41043 1.20521 1.20521 1.20521
time 3.74442 2.47937 1.78568 0.850176 0.850176 0.850176
group 0 0 0 0 0 0
peopl 2.89364 1.70035 0.892838 0.538987 0.538987 0.538987
strategi psycholog misinform inform 2020 social epidem \
misinfodem 2.55053 2.55053 3.57135 2.89364 3.31627 4.01777 0
covid 1.66321 1.66321 2.2321 1.70035 2.07845 2.67852 0
articl 0.554403 0.554403 2.01252 0.892838 1.33926 2.41043 0
commun 1.31385 1.31385 0.819898 0.538987 0.554403 1.20521 0
mechan 1.31385 1.31385 0.819898 0.538987 0.554403 1.20521 0
health 1.31385 1.31385 0.819898 0.538987 0.554403 1.20521 0
strategi 0 0 2.12281 0.819898 1.20521 2.45969 0
psycholog 0 0 2.12281 0.819898 1.20521 2.45969 0
misinform 2.12281 2.12281 0 1.31385 1.07797 0.446419 0
inform 0.819898 0.819898 1.31385 0 0.446419 1.6398 0
2020 1.20521 1.20521 1.07797 0.446419 0 1.31385 0
social 2.45969 2.45969 0.446419 1.6398 1.31385 0 0
epidem 0 0 0 0 0 0 0
pandem 2.45969 2.45969 0.446419 1.6398 1.31385 0 0
time 1.62217 1.62217 0.998224 0.892838 0.446419 1.07797 0
group 0 0 0 0 0 0 0
peopl 0.819898 0.819898 1.31385 0 0.446419 1.6398 0
pandem time group peopl
misinfodem 4.01777 3.74442 0 2.89364
covid 2.67852 2.47937 0 1.70035
articl 2.41043 1.78568 0 0.892838
commun 1.20521 0.850176 0 0.538987
mechan 1.20521 0.850176 0 0.538987
health 1.20521 0.850176 0 0.538987
strategi 2.45969 1.62217 0 0.819898
psycholog 2.45969 1.62217 0 0.819898
misinform 0.446419 0.998224 0 1.31385
inform 1.6398 0.892838 0 0
2020 1.31385 0.446419 0 0.446419
social 0 1.07797 0 1.6398
epidem 0 0 0 0
pandem 0 1.07797 0 1.6398
time 1.07797 0 0 0.892838
group 0 0 0 0
peopl 1.6398 0.892838 0 1.31709e-08
(-0.3387423017564536, 0.9532, -1.6910743913274404)
mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)
Pairwise-distance
2020 covid pandem health polici psycholog \
2020 0 1.64398 1.21093 1.63587 1.38208 1.6757
covid 1.64398 0 1.24569 1.92675 1.26996 1.5689
pandem 1.21093 1.24569 0 1.41086 0.788078 1.17936
health 1.63587 1.92675 1.41086 0 1.46491 1.41224
polici 1.38208 1.26996 0.788078 1.46491 8.33e-09 1.0708
psycholog 1.6757 1.5689 1.17936 1.41224 1.0708 1.666e-08
impact 1.4073 1.31599 0.946785 1.4662 0.602045 1.18626
countri 1.81508 1.02665 1.09741 1.74074 0.976989 1.32184
develop 1.38408 1.33065 0.678531 1.43251 0.368953 1.06407
nation 1.43333 1.36377 0.891159 1.32253 0.424222 1.16005
social 1.6997 1.63894 1.32541 1.67666 1.13705 1.40467
epidem 1.68517 1.65599 1.34219 1.68828 1.03278 1.36759
time 1.37622 1.41466 1.06184 1.495 0.649391 1.14433
group 2.23965 2.09422 1.9243 2.24603 1.74913 2.04504
peopl 1.86356 1.65426 1.24942 1.88704 1.13437 1.53597
impact countri develop nation social epidem \
2020 1.4073 1.81508 1.38408 1.43333 1.6997 1.68517
covid 1.31599 1.02665 1.33065 1.36377 1.63894 1.65599
pandem 0.946785 1.09741 0.678531 0.891159 1.32541 1.34219
health 1.4662 1.74074 1.43251 1.32253 1.67666 1.68828
polici 0.602045 0.976989 0.368953 0.424222 1.13705 1.03278
psycholog 1.18626 1.32184 1.06407 1.16005 1.40467 1.36759
impact 1.17804e-08 1.14809 0.537294 0.615702 1.38141 1.23832
countri 1.14809 1.666e-08 1.02248 1.01999 1.41843 1.34755
develop 0.537294 1.02248 8.33e-09 0.573437 1.17569 1.09679
nation 0.615702 1.01999 0.573437 0 1.13367 1.06065
social 1.38141 1.41843 1.17569 1.13367 0 1.05316
epidem 1.23832 1.34755 1.09679 1.06065 1.05316 3.332e-08
time 0.961153 1.20956 0.798896 0.623022 0.916963 0.984028
group 1.85976 2.03729 1.7955 1.77705 1.78649 2.02774
peopl 1.32558 1.49549 1.14474 1.16066 1.206 1.54428
time group peopl
2020 1.37622 2.23965 1.86356
covid 1.41466 2.09422 1.65426
pandem 1.06184 1.9243 1.24942
health 1.495 2.24603 1.88704
polici 0.649391 1.74913 1.13437
psycholog 1.14433 2.04504 1.53597
impact 0.961153 1.85976 1.32558
countri 1.20956 2.03729 1.49549
develop 0.798896 1.7955 1.14474
nation 0.623022 1.77705 1.16066
social 0.916963 1.78649 1.206
epidem 0.984028 2.02774 1.54428
time 1.666e-08 1.7937 1.20018
group 1.7937 3.332e-08 1.76392
peopl 1.20018 1.76392 0
Pairwise-distance
2020 covid pandem health polici psycholog \
2020 2.98023e-08 1.40859 1.40457 1.39359 1.1861 1.40269
covid 1.40859 0 1.40314 1.39289 1.18071 1.39959
pandem 1.40457 1.40314 2.98023e-08 1.38902 1.18354 1.39676
health 1.39359 1.39289 1.38902 0 1.16427 1.38385
polici 1.1861 1.18071 1.18354 1.16427 1.05367e-08 1.17642
psycholog 1.40269 1.39959 1.39676 1.38385 1.17642 0
impact 1.39073 1.38451 1.38414 1.37391 1.16903 1.38027
countri 1.39416 1.39268 1.38846 1.37928 1.17603 1.38387
develop 1.34013 1.33483 1.33611 1.3187 1.0975 1.33625
nation 1.08223 1.07356 1.08874 1.06366 0.530187 1.08601
social 1.38116 1.37955 1.37681 1.36128 1.1579 1.37572
epidem 1.38005 1.37922 1.3742 1.36229 1.14678 1.36667
time 1.38974 1.38462 1.37977 1.37021 1.16468 1.38332
group 1.39081 1.3877 1.38526 1.37121 1.16239 1.38284
peopl 1.4018 1.40042 1.39753 1.38745 1.17672 1.39234
impact countri develop nation social epidem time \
2020 1.39073 1.39416 1.34013 1.08223 1.38116 1.38005 1.38974
covid 1.38451 1.39268 1.33483 1.07356 1.37955 1.37922 1.38462
pandem 1.38414 1.38846 1.33611 1.08874 1.37681 1.3742 1.37977
health 1.37391 1.37928 1.3187 1.06366 1.36128 1.36229 1.37021
polici 1.16903 1.17603 1.0975 0.530187 1.1579 1.14678 1.16468
psycholog 1.38027 1.38387 1.33625 1.08601 1.37572 1.36667 1.38332
impact 0 1.37441 1.33717 1.06999 1.35463 1.35559 1.36633
countri 1.37441 0 1.30759 1.07975 1.3601 1.3684 1.37383
develop 1.33717 1.30759 0 0.906651 1.31014 1.29463 1.31101
nation 1.06999 1.07975 0.906651 0 1.05625 1.02749 1.0551
social 1.35463 1.3601 1.31014 1.05625 0 1.34363 1.36331
epidem 1.35559 1.3684 1.29463 1.02749 1.34363 0 1.35265
time 1.36633 1.37383 1.31101 1.0551 1.36331 1.35265 0
group 1.36958 1.3713 1.32219 1.05924 1.36177 1.35927 1.36874
peopl 1.38121 1.38798 1.32331 1.08154 1.37895 1.37021 1.38017
group peopl
2020 1.39081 1.4018
covid 1.3877 1.40042
pandem 1.38526 1.39753
health 1.37121 1.38745
polici 1.16239 1.17672
psycholog 1.38284 1.39234
impact 1.36958 1.38121
countri 1.3713 1.38798
develop 1.32219 1.32331
nation 1.05924 1.08154
social 1.36177 1.37895
epidem 1.35927 1.37021
time 1.36874 1.38017
group 2.10734e-08 1.3823
peopl 1.3823 1.49012e-08
(0.5025992380324102, 0.0026, 2.045465473690431)
mantel_test_verticle(model_psy_p,model_psy_paragraph,10)
Pairwise-distance
2020 covid pandem health polici psycholog \
2020 0 2.07845 1.31385 0.554403 0 1.20521
covid 2.07845 0 2.67852 1.65813 0 1.66321
pandem 1.31385 2.67852 0 1.20521 0 2.45969
health 0.554403 1.65813 1.20521 0 0 1.31385
polici 0 0 0 0 0 0
psycholog 1.20521 1.66321 2.45969 1.31385 0 0
impact 0.538987 2.20461 1.83335 0.998224 0 0.892838
countri 0 0 0 0 0 0
develop 0.538987 2.20461 1.83335 0.998224 0 0.892838
nation 0 0 0 0 0 0
misinfodem 3.31627 1.33926 4.01777 2.96288 0 2.55053
articl 1.33926 1.10881 2.41043 1.20521 0 0.554403
commun 0.554403 1.65813 1.20521 0 0 1.31385
mechan 0.554403 1.65813 1.20521 1.31709e-08 0 1.31385
strategi 1.20521 1.66321 2.45969 1.31385 0 0
misinform 1.07797 2.2321 0.446419 0.819898 0 2.12281
inform 0.446419 1.70035 1.6398 0.538987 0 0.819898
impact countri develop nation misinfodem articl \
2020 0.538987 0 0.538987 0 3.31627 1.33926
covid 2.20461 0 2.20461 0 1.33926 1.10881
pandem 1.83335 0 1.83335 0 4.01777 2.41043
health 0.998224 0 0.998224 0 2.96288 1.20521
polici 0 0 0 0 0 0
psycholog 0.892838 0 0.892838 0 2.55053 0.554403
impact 0 0 6.58545e-09 0 3.31522 1.23968
countri 0 0 0 0 0 0
develop 6.58545e-09 0 0 0 3.31522 1.23968
nation 0 0 0 0 0 0
misinfodem 3.31522 0 3.31522 0 0 2.07845
articl 1.23968 0 1.23968 0 2.07845 0
commun 0.998224 0 0.998224 0 2.96288 1.20521
mechan 0.998224 0 0.998224 0 2.96288 1.20521
strategi 0.892838 0 0.892838 0 2.55053 0.554403
misinform 1.61696 0 1.61696 0 3.57135 2.01252
inform 0.554403 0 0.554403 0 2.89364 0.892838
commun mechan strategi misinform inform
2020 0.554403 0.554403 1.20521 1.07797 0.446419
covid 1.65813 1.65813 1.66321 2.2321 1.70035
pandem 1.20521 1.20521 2.45969 0.446419 1.6398
health 0 1.31709e-08 1.31385 0.819898 0.538987
polici 0 0 0 0 0
psycholog 1.31385 1.31385 0 2.12281 0.819898
impact 0.998224 0.998224 0.892838 1.61696 0.554403
countri 0 0 0 0 0
develop 0.998224 0.998224 0.892838 1.61696 0.554403
nation 0 0 0 0 0
misinfodem 2.96288 2.96288 2.55053 3.57135 2.89364
articl 1.20521 1.20521 0.554403 2.01252 0.892838
commun 0 0 1.31385 0.819898 0.538987
mechan 0 0 1.31385 0.819898 0.538987
strategi 1.31385 1.31385 0 2.12281 0.819898
misinform 0.819898 0.819898 2.12281 0 1.31385
inform 0.538987 0.538987 0.819898 1.31385 0
Pairwise-distance
2020 covid pandem health polici \
2020 2.98023e-08 1.40859 1.40457 1.39359 1.1861
covid 1.40859 0 1.40314 1.39289 1.18071
pandem 1.40457 1.40314 2.98023e-08 1.38902 1.18354
health 1.39359 1.39289 1.38902 0 1.16427
polici 1.1861 1.18071 1.18354 1.16427 1.05367e-08
psycholog 1.40269 1.39959 1.39676 1.38385 1.17642
impact 1.39073 1.38451 1.38414 1.37391 1.16903
countri 1.39416 1.39268 1.38846 1.37928 1.17603
develop 1.34013 1.33483 1.33611 1.3187 1.0975
nation 1.08223 1.07356 1.08874 1.06366 0.530187
misinfodem 1.00964 0.998188 0.99604 0.990675 0.666441
articl 1.25618 1.24364 1.23712 1.23471 0.99842
commun 1.3593 1.35413 1.35723 1.33539 1.1385
mechan 0.997926 0.993957 0.991672 0.975204 0.640464
strategi 1.01514 1.01603 1.00632 0.994037 0.674641
misinform 0.998371 0.998127 0.99557 0.974389 0.649831
inform 1.34995 1.34921 1.34654 1.3382 1.11087
psycholog impact countri develop nation misinfodem \
2020 1.40269 1.39073 1.39416 1.34013 1.08223 1.00964
covid 1.39959 1.38451 1.39268 1.33483 1.07356 0.998188
pandem 1.39676 1.38414 1.38846 1.33611 1.08874 0.99604
health 1.38385 1.37391 1.37928 1.3187 1.06366 0.990675
polici 1.17642 1.16903 1.17603 1.0975 0.530187 0.666441
psycholog 0 1.38027 1.38387 1.33625 1.08601 0.998159
impact 1.38027 0 1.37441 1.33717 1.06999 0.978871
countri 1.38387 1.37441 0 1.30759 1.07975 0.990788
develop 1.33625 1.33717 1.30759 0 0.906651 0.89179
nation 1.08601 1.06999 1.07975 0.906651 0 0.470226
misinfodem 0.998159 0.978871 0.990788 0.89179 0.470226 0
articl 1.26125 1.21618 1.23717 1.1515 0.888794 0.762354
commun 1.35026 1.338 1.3383 1.28142 1.044 0.952795
mechan 0.985848 0.971771 0.974543 0.890443 0.43569 0.124054
strategi 1.00808 0.993475 0.98493 0.873182 0.465704 0.164871
misinform 0.991994 0.975257 0.982905 0.894889 0.444944 0.113261
inform 1.34084 1.32679 1.32992 1.2774 1.01059 0.862982
articl commun mechan strategi misinform \
2020 1.25618 1.3593 0.997926 1.01514 0.998371
covid 1.24364 1.35413 0.993957 1.01603 0.998127
pandem 1.23712 1.35723 0.991672 1.00632 0.99557
health 1.23471 1.33539 0.975204 0.994037 0.974389
polici 0.99842 1.1385 0.640464 0.674641 0.649831
psycholog 1.26125 1.35026 0.985848 1.00808 0.991994
impact 1.21618 1.338 0.971771 0.993475 0.975257
countri 1.23717 1.3383 0.974543 0.98493 0.982905
develop 1.1515 1.28142 0.890443 0.873182 0.894889
nation 0.888794 1.044 0.43569 0.465704 0.444944
misinfodem 0.762354 0.952795 0.124054 0.164871 0.113261
articl 0 1.19818 0.754808 0.774005 0.764559
commun 1.19818 2.10734e-08 0.927779 0.936536 0.919373
mechan 0.754808 0.927779 0 0.173639 0.0847542
strategi 0.774005 0.936536 0.173639 0 0.153989
misinform 0.764559 0.919373 0.0847542 0.153989 1.86265e-09
inform 1.19978 1.30395 0.892146 0.90969 0.888665
inform
2020 1.34995
covid 1.34921
pandem 1.34654
health 1.3382
polici 1.11087
psycholog 1.34084
impact 1.32679
countri 1.32992
develop 1.2774
nation 1.01059
misinfodem 0.862982
articl 1.19978
commun 1.30395
mechan 0.892146
strategi 0.90969
misinform 0.888665
inform 1.49012e-08
(-0.12583881303579586, 0.7228, -0.6018039276259918)